Background: The differential effects of rapid cardiac pacing on small and large vessels have not been well-established. The objective of this study was to investigate the effect of pacing-induced acute tachycardia on hemodynamics and arterial stiffness.Methods: The pressure and flow waves in ascending aorta and femoral artery of six domestic swine were recorded simultaneously at baseline and heart rates (HR) of 135 and 155 beats per minutes (bpm) and analyzed by the models of Windkessel and Womersley types. Accordingly, the flow waves were simultaneously measured at carotid and femoral arteries to quantify aortic pulse wave velocity (PWV). The arterial distensibility was identified in small branches of coronary, carotid and femoral arteries with diameters of 300–600 μm by ex vivo experiments.Results: The rapid pacing in HR up to 135 bpm reduced the total arterial compliance, stroke volume, systemic pulse pressure, and central systolic pressure by 36 ± 17, 38 ± 26, 29 ± 16, and 23 ± 12%, respectively, despite no statistical difference of mean aortic pressure, cardiac output, peripheral resistance, and vascular flow patterns. The pacing also resulted in a decrease of distensibility of small muscular arteries, but an increase of aortic distensibility. Pacing from 135 to 155 bpm had negligible effects on systemic and local hemodynamics and arterial stiffness.Conclusions: There is an acute mismatch in the response of aorta and small arteries to pacing from basal HR to 135 bpm, which may have important pathological implications under chronic tachycardia conditions. Epidemiologic data show that hypertension and atrial fibrillation (AF) often coexist  Bergel, . The patTotal arterial compliance and peripheral resistance are two key parameters that have been widely used to characterize systemic hemodynamics . The physiological implications of initial rapid pacing are discussed along with the limitations and significance of the study.We hypothesize that pacing-induced acute tachycardia decreases the acute stiffness of aorta and increases the acute stiffness of small arteries in some pressure range, which results in an acute mismatch. Moreover, pacing-induced acute tachycardia reduces the total arterial compliance, systemic pulse pressure (PP), central systolic pressure, and stroke volume (SV) in some pressure range despite no statistical difference of mean aortic pressure (MAP), cardiac output (CO), peripheral resistance, and transient flow patterns in aorta and peripheral arteries. To test these hypotheses, the simultaneous measurement of blood pressure, flow, and cross-sectional area (CSA) was performed in ascending aorta and femoral artery of six domestic swine at baseline and during right atrial pacing to HR of 135, 155, and 170 beats per minutes (bpm). Analytic models of Windkessel and Womersley types were used to carry out transient hemodynamic analysis based on these experimental measurements. Aortic pulse wave velocity (PWV) was determined using two simultaneously measured flow waves. In addition to the in vivo hemodynamic pacing measurements and six controls with similar weights for ex vivo measurements of dynamic pressure and diameter waves in small arteries of 300–600 μm in diameter. All animal experiments were performed in accordance with Indiana University, Purdue University, Indianapolis, consistent with the NIH guidelines  on the protection of animals used for scientific purposes. The experimental protocols were approved by the Institutional Animal Care and Use Committee of Indiana University.Studies were performed on six domestic swine weighing 71 ± 8 kg for The animal preparation was similar to previous studies  was screwed into the wall of right atrium and connected to a pacemaker (Medtronic Enpulse E2DR01) which was placed into a subcutaneous pocket. The ascending and descending aortas and femoral artery were dissected. Perivascular flow probes  were mounted on these arteries to measure the volumetric flow rate. Flow and pressure were continuously recorded using a Biopac MP 150 data acquisition system. The cross-sectional area (CSA) of arteries was determined using ultrasound (Philips IE33 ultrasound system).The heart rate was paced to 135, 155, and 170 bpm as compared with basal HR of about 90 bpm (range of 80–105 bpm). The animals were allowed to recover to basal HR between consecutive pacing sessions. Electrocardiography (ECG) signals were used to monitor HR. The aortic, carotid and femoral flow rates were measured simultaneously. The femoral arterial pressure was measured by the pressure transducer connected to the 7Fr. sheath. The aortic pressure was determined when the sheath was advanced to aorta under fluoroscopy.We have shown an abrupt decrease of blood pressure and flow after rapid pacing  and then recovered close to baseline after about 3–5 min of pacing (recovery period)  and then isolated from various positions  and dissected free of fat and connective tissue after euthanasia. The side branches were ligated with suture under dissection microscope in 4°C HEPES PSS . An artery specimen was mounted on the two cannulas in an organ bath chamber containing PSS solution. One cannula was connected with a pressure transducer through a Y tube, while the other was connected with a bottle of PSS solution that induced sawtooth flow wave by a piston pump. The temperature in the bath and bottle was gradually increased to 37°C in 10 min. The vessel was stretched close to the in vivo length . The image of the vessel was displayed on screen with a CCD camera mounted on a stereo microscope and the changes of outer diameter were measured with dimensional analysis software . The pressure transducer and diameter tracings were interfaced into a computer by a data acquisition system , which monitored transient changes of pressure and diameter. The time-averaged pressure over a cardiac cycle equaled to 80 mmHg. We determined the changes of the arterial distensibility as the frequency increases from 1 to 3 Hz (by a step of 0.5 Hz) to mimic HR from 60 to 180 bpm.Similar to previous studies . MAP and PP equal to Pmean and the difference of systolic and diastolic pressures, respectively, in ascending aorta. The CO was computed by Qmean×60 s and the SV was calculated as the ratio of CO to HR. The classical two-element Windkessel model  is the input impedance, ω the angular frequency after Fourier transformation, C the total arterial compliance , and R the peripheral resistance (= Pmean/Qmean). The peripheral resistance was obtained directly from the measured pressure and flow waves and the total arterial compliance was estimated by the pulse pressure method  was determined using the three-element Windkessel model , Zc is estimated by minimizing the error between the input impedance by Fourier transformation of the ratio of the measured pressure to flow waves where pforward(t), qforward(t)] and backward  pressure and flow waves are given as Westerhof et al. , is given as:Similar to a previous study , τ, and oscillatory shear index (OSI) for pulsatile blood flow can be written as:where 3, respectively. The forward and backward WSS were computed, based on qforward(t) and qbackward(t), respectively.The viscosity (μ) and density (ρ) were assumed to be 4.0 cp and 1.06 g/cmAortic PWV is the velocity at which the arterial pulse propagates through the vessel and used clinically as a measure of arterial stiffness. Similar to a previous study  between baseline and various HR, where p- < 0.05 represented statistically significant differences.The measurements were repeated five times and averaged at each HR per animal. The mean and standard deviation (mean ± SD) were computed by averaging over all animals in each HR group. One Way Repeated Measures ANOVA (SigmaStat 3.5) was used to compare the various parameters , respectively, as HR increased from baseline to 135 bpm. The peripheral resistance was relatively unchanged (p > 0.25) as HR increased from baseline to 135 bpm while the total arterial compliance distal to aorta and femoral artery was decreased by 36 ± 17 and 29 ± 14% (p < 0.05), respectively, as shown in Table Figures p < 0.05) as HR increased from baseline to 135 bpm, as shown in Table p = 0.15) between 135 and 155 bpm. The characteristic impedance in the descending aorta had values of 0.31 ± 0.22, 0.27 ± 0.13, and 0.26 ± 0.13 mmHg·s/ml for baseline, 135, and 155 bpm, respectively. On the other hand, Figure −3 × mmHg−1) as the frequency increases in isolated small branches of coronary (square mark), carotid (triangle mark) or femoral (circle mark) arteries. There was statistically significant difference (p < 0.05) in arterial distensibility between 1.5 Hz (mimicking the HR of 90 bpm) and higher frequencies . Moreover, Figure The aortic  PWV was reduced (peak) are 6,000 and 4,400 for baseline and 155 bpm, respectively, while Womersley numbers (Wo) are 8.9 and 11.7. Moreover, Figures At baseline, Figure The major finding of the present study was that aortic PWV, determined by the validated techniques in Mohiaddin et al.  and ButlIt has been documented that pharmacological HR lowering in patients with high BP can result in adverse cardiovascular outcomes /[(As+Ad)(PES−Pd)], where As and Ad refer to the systolic and diastolic areas under the pressure curve . Since the value of Ad/(As+Ad)/(PES−Pd) remains relatively unchanged, the 38 ± 26% decrease of SV is the major determinant for the 36 ± 17% decrease of total arterial compliance. In contrast, a decrease of systemic PP (29 ± 16%) due to the elevated HR may be beneficial for the cardiovascular system because a high value of systemic PP is known to correlate with cardiovascular mortality and morbidity  distal to the ascending aorta, but remain the peripherical resistance  in comparison with the baseline, as shown in Table p < 0.05) with elevated HR from baseline to 135 bpm, which is consistent with the previous studies  in descending aorta was consistent with the measured aortic PWV given Zc∞PWV  was found as the frequency increased from 1.5 to ≥ 2 Hz. The interaction of incident and reflected pressure and flow waves determines the actual waves, which depends on the mechanical properties of large elastic arteries, small muscular arteries, and small arterioles  > 250·Wo (Wo < 11.7), the blood flow in aorta should result in turbulence during the decelerating phase of pulsatile flow, but not during the accelerating phase (Nerem and Seed, The accelerating and decelerating phases of pulsatile flow are clearly distinguishable periods in a cardiac cycle, as shown in top curves in Figures The low time-averaged WSS and high OSI are thought to result in endothelial dysfunction, monocyte deposition, smooth muscle cell proliferation, microemboli formation, and so on (Fan et al., The arterial distensibility were measured in isolated small arteries with diameters of 300-600 μm, which needs to be incorporated into the cardiovascular system for a more systematic analysis. Hence, the hemodynamic analysis in the entire arterial tree including large arteries, small arteries and small arterioles should be implemented to help understand the contribution of both acute and chronic high HR to arterial stiffness and atherosclerosis (Huo and Kassab, in vivo and ex vivo experimental measurements and hemodynamic analysis. The acute increase in HR up to 135 bpm resulted in significant increase/decrease of the distensibility of aorta/small muscular arteries and a mismatch in the response of aorta and small arteries. The mismatch between aorta and small muscular arteries in high HR can be a risk factor for adverse cardiovascular events. The pacing also reduced the total arterial compliance, SV, systemic PP, and central systolic pressure but had no statistical significant effect on MAP, CO, peripheral resistance, and vascular flow patterns. Finally, pacing from 135 to 155 bpm had negligible effect on systemic hemodynamics and arterial stiffness.This study investigated the role of rapid pacing on systemic and local hemodynamics and arterial stiffness through Data analysis were performed by YH at the college of engineering, Peking University and HC at the California Medical Innovations Institute. Paper was drafted and revised by YH and GK at the California Medical Innovations Institute.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Village health workers (VHWs) in five villages in Bugoye subcounty  provide integrated community case management (iCCM) services, in which VHWs evaluate and treat malaria, pneumonia, and diarrhoea in children under 5 years of age. VHWs use a “Sick Child Job Aid” that guides them through the evaluation and treatment of these illnesses. A retrospective observational study was conducted to measure the quality of iCCM care provided by 23 VHWs in 5 villages in Bugoye subcounty over a 2-year period.Patient characteristics and clinical services were summarized using existing aggregate programme data. Lot quality assurance sampling of individual patient records was used to estimate adherence to the iCCM algorithm, VHW-level quality (based on adherence to the iCCM protocol), and change over time in quality of care .For each of 23 VHWs, 25 patient visits were randomly selected from a 2-year period after iCCM care initiation. In these visits, 97% (150) of patients with diarrhoea were treated with oral rehydration and zinc, 95% (216) of patients with pneumonia were treated with amoxicillin, and 94% (240) of patients with malaria were treated with artemisinin-based combination therapy or rectal artesunate. However, only 44% (44) of patients with a negative rapid test for malaria were appropriately referred to a health facility. Overall, 75% (434) of patients received all the correct evaluation and management steps. Only 9 (39%) of the 23 VHWs met the pre-determined LQAS threshold for high-quality care over the 2-year observation period. Quality of care increased significantly in the first 6 months after initiation of iCCM services (p = 0.003), and then plateaued during months 7–24.Quality of care was high for uncomplicated malaria, pneumonia and diarrhoea. Overall quality of care was lower, in part because VHWs often did not follow the guidelines to refer patients with fever who tested negative for malaria. Quality of care appears to improve in the initial months after iCCM implementation, as VHWs gain initial experience in iCCM care.The online version of this article (10.1186/s12936-018-2241-5) contains supplementary material, which is available to authorized users. Village health worker (VHW) programmes undertake a range of different activities in different settings, with varying levels of focus on health promotion, case finding, adherence support, clinical care, and other goals. In many programmes, VHWs provide health education as well as initial assessment and referral to a health facility, but are not trained to administer medical treatment. More recently, interest has increased in equipping VHWs to provide assessment and curative treatment for young children, using variants of integrated community case management (iCCM). In iCCM programmes, VHWs provide diagnosis and treatment of malaria, pneumonia, and sometimes diarrhoea in children under 5 years of age using a standardized assessment and treatment algorithm.iCCM has the potential to increase access to prompt care, thereby decreasing morbidity and mortality. In addition to its potential benefits, iCCM also carries important risks of harm. Empowering VHWs to provide anti-malarials and antibiotics could result in overtreatment and undertreatment, both of which pose substantial risk. A retrospective observational study was conducted to assess the quality of iCCM care provided by 23 VHWs in five villages over a 2-year period from March 2013–February 2015 in a pilot iCCM programme in Bugoye subcounty, a rural part of Kasese district in Western Uganda.There are several reasons for further evaluation of iCCM quality of care. First, apart from one study that examined only competence in performing rapid diagnostic tests (RDTs) for malaria , prior sThe programme described here relies on a wholly volunteer VHW workforce, most of whom have limited formal education, though all are literate in the local language of Lukonjo. VHWs received 3 days of general training and 5 days of iCCM training , as well as half-day refresher trainings on a quarterly basis. VHWs use the iCCM protocol, called the “Sick Child Job Aid”, to determine the proper care for each patient  were collected in Microsoft Excel format and combined into a single database. Missing values and outliers were compared with the original paper records, with correction of the database as appropriate. The combined database was summarized using Stata Version 12 . Descriptive results are reported here to contextualize the quality of care data by describing patient volume.A stratified, unweighted sampling design was used to measure quality at the level of individual VHWs, and to provide optimal data for the primary analytic outcome . To ensure a sufficient sample size for this approach, lot quality assurance sampling (LQAS) methods were used to establish the requisite stratum sample size . While originally intended as a rapid and cost-effective quality assurance method for industrial production , LQAS haVHWs’ Sick Patient Registers were used to create a full sampling frame for each VHW, with 25 patient encounters per VHW randomly sampled. A customized research electronic data capture (REDCap) database was used for data entry, hosted by Partners Healthcare . This daLogic statements in Stata were used to determine whether each patient received appropriate care according to iCCM protocols. To verify the decision rules and logic statements used for automated determination of iCCM protocol compliance, 125 patient encounters were reviewed and hand-graded using the iCCM protocol, with 100% agreement with the automated determinations.Disease-specific quality metrics were calculated for the overall programme. Quality metrics included: (1) adherence to correct diagnostic protocol ; (2) appropriate prescribing practices ; (3) appropriate referral practices in keeping with iCCM guidelines; (4) the proportion of VHWs currently providing high-quality care using the LQAS decision rule cut-off.Change over time in the quality of iCCM care provided by VHWs was then assessed, to measure how VHWs’ level of experience impacted quality of care. Two competing trends may affect this relationship: VHWs may gain confidence and competence over time in using the iCCM algorithm; alternatively, adherence to the algorithm may decline as VHWs become further removed from their initial iCCM training. Logistic regression models, with a generalized estimating equations (GEE) and robust standard errors to account for correlation by VHW , 12, werThree main regression models were used to test this hypothesis and optimally explain the relationship of interest: (1) categorizing programme duration into four, 6-month intervals; (2) modelling time as a continuous variable; and (3) modelling time as a continuous variable with a spline knot at 6 months. Quasi-likelihood under the independence model criterion (QIC)—a modification of the Akaike information criterion (AIC) so that it can be applied to GEE models—was used to assess goodness of fit of the two continuous models . FinallyIn the overall iCCM pilot programme, 23 VHWs in 5 villages completed 5462 patient visits over 2 years; 2102 (38%) patients received evaluation/treatment within 24 h (Table Each VHW saw an average of 237 patients range 105–441)—an average of 126 presenting with fever (range 43–316), 99 with cough/fast breathing (range 40–221), and 63 with diarrhoea (range 18–139); see Table 05–441—anOf the 575 patient visits sampled, 52% (286) of patients were female and 48% (265) were male, with a mean age of 31 months (range 2 months–5 years); see Table In regard to presenting complaints and outcomes, 293 (51%) patients presented with fever, 237 (41%) with cough/fast breathing, and 155 (27%) with diarrhoea  correctly received an RDT for malaria. Additionally, there were 73 patients who did not have fever recorded as a presenting complaint, but who nonetheless received an RDT. Of 255 patients with a positive RDT for malaria, 240 (94%) received correct management  correctly had their respiratory rate recorded. Of the 228 patient visits in which an elevated respiratory rate was documented, 216 (95%) were treated appropriately with amoxicillin  were managed appropriately with ORS and zinc Table . AdditioOf 11 patient visits with “danger signs” recorded, all 11 were documented as having been appropriately referred to a health centre, but only four had appropriate pre-referral treatment documented Table . HoweverAfter combining all the decision rules for correct management, over the 2-year period studied 75% (434) of patient visits received correct evaluation and treatment. The proportion of patient visits with correct management was lowest in months 1–6 of the iCCM pilot, with 63% (96) of patients managed correctly, compared with 79% (116) in months 7–12, 76% (117) in months 13–18, and 85% (105) in months 19–24. This trend was driven substantially by improved performance in the treatment of malaria and pneumonia. In months 1–6, VHWs followed appropriate malaria treatment protocols in 77% (51) of relevant visits, compared with 93% (74) in months 7–12, 97% (66) in months 13–18, and 94% (50) in months 19–24. Similarly, in months 1–6 VHWs followed appropriate pneumonia treatment protocols in 73% (52) of visits, compared with 91% (48) in months 7–12, 91% (62) in months 13–18, and 92% (54) in months 19–24.At the individual VHW level, according to the LQAS decision rule cut-off of 20 patients (out of 25), 9 VHWs are classified as providing high-quality care over the 2-year period, while 14 are classified as not yet providing high-quality care. The median proportion of patient visits managed correctly by each VHW was 80%, with a range of 32–92%.Graphical depiction of the data identified a trend toward improved quality of care during the first 6 months after initiation of iCCM care, and a levelling off of the relationship thereafter Fig. . The firIn this study, quality of care was high for the core steps involved in managing uncomplicated malaria, pneumonia, and diarrhoea. These results generally accord with prior research. A recent systematic review of malaria care documented very high quality of care by VHWs, with 95–100% correct interpretation of RDTs, and appropriate treatment of patients with positive RDTs in over 90% of cases . QualityThis study shows much lower adherence to referral guidelines for patients with fever who had negative RDTs; indeed, this was the single most common error made by VHWs, comprising nearly 40% of the patient visits with incorrect management. Similarly, prior studies have shown low adherence to referral guidelines, with only 18.2–47.1% of patients receiving appropriate referral . This erInappropriate use of anti-malarials and antibiotics  was relatively low in this study, with 4% inappropriate use of ACT and 10% inappropriate use of amoxicillin. These rates are slightly higher than in a recent study in Ethiopia , and broThe LQAS results demonstrate substantial variation in quality among VHWs. As a monitoring tool, this may offer a useful means of identifying VHWs who would benefit from additional training and mentorship. While the final LQAS result, in which only 9 of 23 VHWs are classified as providing high-quality care, may seem disconcerting, the analytic results of this study document a significant trend toward improvement in quality in the initial months of iCCM implementation. Due to the sample size, the LQAS assessment could not be repeated in a rigorous way using only months 7–24 to compare these results. Similarly, the LQAS assessment could not be repeated without the inclusion of RDT-negative patients who were not referred.The analytic results of this study demonstrate an encouraging improvement over time in VHWs’ competence during the initial 6-month period after iCCM implementation, increasing from approximately 50–80% correct management rates, after which point the proportion of patients managed appropriately seemed to plateau.This study has a number of limitations, in part resulting from our use of routine clinical records as the data source. First, and perhaps most importantly, record review assesses quality of documented care, which may differ from the quality of actual care provided. Additionally, only limited validation of VHWs’ documentation has occurred in this programme. The methods used here cannot assess whether VHWs performed RDTs correctly or measured respiratory rate accurately, so in that respect these results may overestimate quality of care. Conversely, in a programme in which some VHWs have limited literacy skills, some patients may have received correct care but incorrect documentation. For instance, patients who received ORS or zinc but did not have diarrhoea listed as a presenting complaint are considered to have received incorrect management, even though this may have resulted from a record-keeping error. Prior research comparing record review (as employed here) with direct observation of VHWs and re-examination of patients by a trained clinician suggests that the two methods yield broadly similar results, with record review overestimating quality for some metrics .Second, not all relevant dimensions of quality can be assessed with the available data, such as correct dosing of medications or appropriate instructions for caretakers. Likewise, since the specific “danger sign” is not recorded on the clinical form used, it is impossible to characterize fully the management of this subset of patients, who likely face the highest risk of morbidity and mortality. The available data also do not provide any information on completion of referrals.Third, for the analytic outcome of change in quality over time, the small number of VHWs in the iCCM pilot programme as well as our data structure prevent further analysis of other factors, such as education level or patient volume. Additionally, there may be important trends in quality of care over time that arise outside the 2-year period observed here.Finally, due to the lack of a reliable unique identifier, it is possible that two different illness episodes for the same individual are included in the sample of patient visits. However, the impact of this issue is likely small, since the two visits for a given patient are not necessarily correlated with respect to quality of care, and since a GEE model with robust standard errors was used to account for non-independence of the data.Despite its limitations, this study yields several significant practical results. First, it provides initial results on the trend in quality of iCCM care over time—a topic highly relevant to the training and supervision needs of VHWs as iCCM programmes expand in Uganda and elsewhere. Further evaluation may clarify the impact of patient volume on this trend, with implications for the size of the catchment area assigned to each VHW. Further research using a larger sample size would also allow for evaluation of VHW-level factors such as level of formal education completed. Second, from a practical standpoint, it demonstrates the feasibility of measuring iCCM quality of care using routine data. LQAS requires additional data entry, but the relatively small sample sizes make it less costly than more exhaustive data entry of all patient encounters. Third, this research demonstrates opportunities for the Ugandan Ministry of Health to improve iCCM protocols and training by identifying common errors. Fourth, on a broader level, it bolsters the evidence for iCCM programmes given the relatively high quality of care for uncomplicated malaria, pneumonia, and diarrhoea, as well as the trend toward improvement over time.Additional file 1: The Republic of Uganda Ministry of Health Sick Child Job Aid (translated in English and Lukonjo for use in Bugoye).


Sulfitobacter, whereas the northern Chukchi bacterial community is dominated by SAR clades, Flavobacterium, Paraglaciecola, and Polaribacter genera associated with low nutrients and sea ice conditions. The frontal region, located on the boundary between the southern and northern Chukchi, is a transition zone with intermediate physical and biogeochemical properties; however, bacterial communities differed markedly from those found to the north and south. In the transition zone, Sphingomonas, with as yet undetermined ecological characteristics, are relatively abundant. Latitudinal distributions in bacterial community composition are mainly attributed to physical and biogeochemical characteristics, suggesting that these communities are susceptible to Arctic environmental changes. These findings provide a foundation to improve understanding of bacterial community variations in response to a rapidly changing Arctic Ocean.The western Arctic Ocean is experiencing some of the most rapid environmental changes in the Arctic. However, little is known about the microbial community response to these changes. Employing observations from the summer of 2017, this study investigated latitudinal variations in bacterial community composition in surface waters between the Bering Strait and Chukchi Borderland and the factors driving the changes. Results indicate three distinctive communities. Southern Chukchi bacterial communities are associated with nutrient rich conditions, including genera such as  Recent Arctic warming is strongly linked to declining sea ice extent, suggesting a strong positive ice-temperature feedback3. As a result, annual sea ice extent has declined rapidly (3.5–4.1% per decade since 1979), which has resulted in physical and biogeochemical changes in the Arctic Ocean2. For instance, freshening of the upper Arctic Ocean enhances stratification and inhibits vertical mixing6, which in turn limits the transfer of deep ocean nutrients into the euphotic zone7. Ultimately, these physical changes will have a critical impact on Arctic primary productivity12.Arctic air temperatures have risen twice as fast as the global average (~0.7 °C) since the mid-20th century13. This ocean has experienced the most rapid sea ice retreat14, which has been driven by heat transport from Pacific waters17. Relatively warm, fresh, and nutrient-rich Pacific waters enter the western Arctic Ocean through the Bering Strait to the Chukchi Sea, which is a shallow (average depth 50 m) and wide (surface area 620 × 103 km2) sea20. During the summer open water season, latitudinal differences in the physical and biogeochemical features of the western Arctic surface water are apparent from the Bering Strait to the Chukchi Borderland21. Relatively low latitude regions  are primarily driven by Pacific waters that supply nutrients and heat, and are among the world’s most productive ocean regions24. Conversely, the higher latitude regions  are relatively cold, fresh, and oligotrophic because the surface layer is highly influenced by freshwater inputs from melting ice and rivers through the Beaufort Gyre. Intermixing of the two surface water masses in the western Arctic has produced a physicochemical frontal zone in the Chukchi Sea5.The Arctic Ocean is comprised of two distinct oceanic regions, distinguished by the presence (western Arctic) or absence (eastern Arctic) of Pacific water within the halocline. The western Arctic Ocean is geographically comprised of the Chukchi, East Siberian and Beaufort Seas, the Canadian Arctic Archipelago, and the Canadian Basin Fig. 13. This 27. Previous studies have shown that large-size phytoplankton groups and a high chlorophyll-a (Chl-a) biomass are associated with high nutrient Pacific waters in lower latitude regions, while small-size phytoplankton groups and a low Chl-a biomass are attributed to the oligotrophic conditions of higher latitude regions21. A latitudinal gradient of abundance and diversity in the summer zooplankton community of the western Arctic has also been reported26.The latitudinal gradient of surface water physical and biogeochemical features in the western Arctic also affects the marine planktonic ecosystem32. Recent studies have reported that the composition and relative abundance of bacterial communities in the western Arctic Ocean are largely determined by physical and biogeochemical water properties, such as temperature, salinity, and nutrients, indicating that microbial communities are highly susceptible to environmental changes37. However, little is known about changes in the latitudinal distribution of bacterial community compositions in response to western Arctic environmental changes38. Based on the studies cited above, we expect that future changes in the western Arctic Ocean will have a profound impact on the latitudinal gradient of physical and biogeochemical features and will in turn alter the Arctic marine ecosystem. To predict the response of western Arctic marine ecosystems to future changes, an investigation of the latitudinal patterns of bacterial community composition and their physical and biogeochemical properties is vital.Bacterial communities are a key component of the marine ecosystem because they are responsible for modifying and decomposing organic matter, supporting higher trophic levels and driving biogeochemical cyclesHere, we present (1) an investigation into the latitudinal distribution of physical-biogeochemical-bacterial community features in western Arctic surface waters during the summer open water season, and (2) a discussion of the environmental parameters governing the latitudinal distribution of the bacterial community. The goal is to provide significant insight into latitudinal gradients in marine ecosystem alterations in response to rapid Arctic climate change.26  samples were collected from the Bering Strait to the Chukchi Borderland in the western Arctic Ocean at 12 stations occupied by the ice breaker RV Araon , and silicate (SiO2)) were analyzed in the onboard laboratory using a continuous flow auto analyzer . In this study, dissolved inorganic nitrogen (DIN) represents the sum of ammonium, nitrite, and nitrate. Cells for Chl-a analysis were filtered onto 25 mm Whatman GF/F filters, extracted in 90% acetone at 4 °C for 24 hours, and quantified using a Turner Designs fluorometer .Physical and biogeochemical parameters were measured at each station. The vertical profiles of temperature and salinity were measured using the CTD  and density was derived from the same. Nutrient samples , and the quality of the extracted DNA was checked using NanoDrop . After performing quality control (QC), the samples were used for library preparation. The 16S rRNA genes were amplified using 16S V3-V4 primers. The primer sequences were as follows: 16S V3-V4 primer (16S Amplicon PCR Forward Primer 5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG) and (16S Amplicon PCR Reverse Primer 5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC). Polymerase chain reaction (PCR) products were purified, and a subsequent limited-cycle amplification step was performed to add the multiplexing indices used to construct the library. The extracted DNA samples were amplified and analyzed using llumina Miseq (Miseq Control Software v2.4.1.3) and Real Time Analysis (RTA v1.18.54.0). Then, 16S tagging DNA fragmentation (100‒500 bp) was performed using the quantitative kit for marine sample DNA. Primers were prepared for all DNA fragments, amplified using an adapter, and subjected to library QC using a bioanalyzer and clustering.https://qiime2.org) to assemble paired end reads into tags according to their overlapping relationship. In the pre-processing step, the primer was removed, and then demultiplexing and quality filtering (Phred ≥20) were applied39. USEARCH7 was used to perform denoising and chimera detection/filtering in operational taxonomic units (OTUs) grouping40. Then, the Silva132 database was used to determine the OTUs with 97% similarity using UCLUST and the open-reference analysis method, and determined the OTU identifier. OTU table was normalized dividing each OTU by the 16S copy number abundance, and in turn normalization of OTU table was performed. After filtering the generated OTU table using the Biological Observation Matrix (BIOM) format, the resulting sequences were clustered into OTUs based on a similarity threshold of ≥97% using Python Nearest Alignment Space Termination (PyNAST)41.Sequencing data were processed using QIIME1.9.1 . A Hellinger-transformation was used for the statistical analysis of bacterial community relative abundance. For clustering analysis, the Bray-Curtis distance . In particular, nitrogen (N)-deficiency (DIN:PO4 < 6.33) was prominent in surface waters  compared to the NC  and the NC 21.Correlation analysis among environmental variables  was from 4–29% (the relative abundance of Rhodobacteraceae: 11–52%) throughout the study area but the relative abundance was the highest in the eutrophic conditions of the SC , a gas with a cooling effect on the Earth’s climate55, through the assimilation of dimethylsulfoniopropionate (DMSP) released by the decomposition of organic matter in the water column after a phytoplankton bloom57. Fluviicola and Persicirhabdus, which are known to favor organic matter enriched conditions53, were also found in the SC.To characterize the differences in bacterial community compositions with latitude, the relative abundance (%) of bacterial communities at the class level was analyzed Fig. . Alphaprude Fig. . At the ude Fig. . Overall SC Fig. . An earlFlavobacterium, and Polaribacter were relatively more abundant in the NC  as compared to SC and the FZ (<~2%).Several taxa, including SAR , SAR86 clade, SAR92 clade,  NC Fig. . SAR is es Table 36,37,49,Polaribacter (of the class Bacteroidia) was one of the dominant groups in the study area. However, the relative abundance was higher in the NC (10 ± 6%) than in the SC and FZ (4 ± 5%)   Table . In addi%) Table . These r%) Table . As the ies Fig. , formed 4, DIN:PO4, SiO2, and Chl-a, Fig. To investigate the influence of environmental factors on latitudinal bacterial community distribution we conducted a correlation analysis between the relative abundances of the top 23 genera and environmental parameters , SAR86 clades, Paraglaciecola, AEGEAN 169 marine group, Flavobacterium, Polaribacter, Planktomarina, and SAR92 clades, which show mostly negative correlations with environmental variables. As found by earlier studies37, oligotrophic-type SAR clades were dominant in NC surface waters due to N-deficient conditions , while other Altererythrobacter are positively correlated with PO4 . Overall, FZ bacterial compositions differ markedly as compared to those of SC and NC, despite the intermediate physical and biogeochemical characteristics of the region  frontal mixing forming unique bacterial communities adapted to an environment with large gradients in physical and biogeochemical properties. These speculations require further examination to improve our understanding of changes in bacterial community composition in the rapidly changing Arctic.Dominant genera in the FZ are 11% Fig. . Sphingo17 and increasing sea-ice melt water34. Based on the results of this study, we can derive potential scenarios as to how the bacterial community compositions revealed in this study will be altered by these phenomena in the western Arctic Ocean in the future.Rapid environmental changes in the Arctic Ocean may influence Arctic bacterial community compositions both directly and indirectly. Among the changes observed in the region, two phenomena have received considerable attention; increasing inflow of warm Pacific waters64, it is likely that the productive SC region will expand and the FZ will move northward, leading to nutrient enrichment in the western Arctic  are likely to have a potential for supporting the occurrence of nitrogen fixation (Fig. If the impact of warm and nutrient-rich Pacific water inflows dominatestic Fig. . In respers Fig. . In thision Fig. . HoweverSulfitobacter. The NC was characterized by extremely oligotrophic surface waters and strong stratification due to the influence of sea-ice melt with bacterial communities dominated by SAR clades, Flavobacterium, Paraglaciecola, and Polaribacter. The FZ was located at the boundary between the SC and NC and represented a transition zone with intermediate physical and biogeochemical properties. However, bacterial communities differed markedly as compared to both the SC and NC. Sphingomonas, whose ecological characteristics are not yet known, were relatively abundant. Results from the present study did not allow us to determine whether the unique bacterial community in the FZ can be attributed to an environmental adaptation or an extension of the subsurface bacterial community. We look to future investigations that would include sampling for microbial community composition over the entire water column and in other regions of the western Arctic to answer this question. Results from this study will aid in our understanding of bacterial community alterations in response to a rapidly changing Arctic Ocean.The latitudinal distribution of physical-biogeochemical-bacterial community features in western Arctic surface waters during summer 2017 was investigated. Results indicated that surface water bacterial communities were influenced by physical and biogeochemical characteristics in the western Arctic Ocean. We identified three distinctive groups associated with different environmental variables in the surface waters of the study area. The SC was relatively warm, saline, and productive, and its bacterial communities were dominated by phytoplankton-derived decomposers such as Supplementary InformationSupplementary Table S2


Water sources from the lower reaches of the Heihe River northwest China, located in an arid area impacted by environmental stresses, have promoted changes to the local soil and plant conditions; however, our understanding of variations and drivers of soil bacterial communities in an arid inland river basin remains unclear. Therefore, we collected 39 soil samples from a riparian oasis zone (ROZ) to the circumjacent desert zone (CDZ) at the lower reaches of Heihe River to evaluate bacterial communities based on the 16S rRNA gene data. We found that the bacterial community composition differed between ROZ and CDZ habitats, with significantly higher relative abundance of the phyla Gemmatimonadetes and Acidobacteria in ROZ, whereas the abundance of the phyla Actinobacteria and Deinococcus–Thermus was greater in CDZ. The difference in the bacterial community was almost entirely generated by the species turnover rather than the nestedness among all samples. In addition, we found that bacterial α-diversity index showed no significant difference between ROZ and CDZ habitats. The distance-decay analysis showed that spatial distance, plant community, soil property, and plant functional trait were correlated with bacterial community variations. However, the variation partition analysis (VPA) revealed that both soil properties and plant community strongly explained the difference  compared with plant functional traits in bacterial β-diversity and species turnover. Based on a co-occurrence network analysis, we found that the bacterial network of ROZ, which had more negative correlations, higher average connectivity, shorter average path length, and smaller modularity, was more complex than the network of CDZ. This suggested that the bacterial community was more stable and less vulnerable to change in the ROZ habitat than in the CDZ habitat. Overall, our findings suggest that the heterogeneity of soil properties and plant community collectively affect the structure of the soil bacterial community in an arid inland river basin. However, the influence of plant functional traits on the variation of the bacterial community depends on soil properties and plant community. Soil microbial communities play a critical role in regulating the functions and stability of an ecosystem . ImproviDistribution patterns and drivers of soil microbes have attracted extensive attention over the past several years. Previous studies have provided evidences that environmental heterogeneity could drive the composition and distribution of the microbial community . For insIn addition to the environmental factors, microbial interaction is another selection force that may contribute to microbial community assembly. Thus, clarifying their interactions can provide insight into microbial diversity and function . EcologiPopulus euphratica, with sparse understory vegetation , whereas communities in the CDZ are mainly dominated by xeric shrubs, such as Reaumuria soongarica and Nitraria tangutorum. Thus, this condition provided a unique opportunity to investigate the soil bacterial community under different dominant vegetation in the same climatic conditions. In addition, soil environmental conditions may lead to a change in the soil bacterial community  How do the diversity and composition of the bacterial community change vertical to the arid inland river and what is the relative contribution of the turnover and nestedness to the bacterial β-diversity? (2) How do the plant attributes, soil properties, and spatial factors drive the biogeographical distribution of bacteria? and (3) What is the difference in the co-occurrence networks of the bacterial community between the ROZ and the CDZ?The study was conducted during the peak growing season (July) in the lower reaches of the Heihe River in Ejina Banner, Inner Mongolia , which is a typical arid inland river basin in northwest China . The sampling area is characterized by an arid continental climate, with hot summer and cold winter. The mean annual temperature is 8.4°C, and the mean temperature is 26.4°C in July, with the extremely highest temperature reaching 43.1°C and daily temperature range reaching 14–20°C, whereas the mean temperature is −11.9°C in January, with the extremely lowest temperature reaching −37.6°C. The mean annual precipitation is approximately 37.9 mm, which mostly occurs during June to August, but the precipitation is almost unavailable because each precipitation is less than 5 mm. In addition, the annual potential evaporation is approximately 100 times the annual precipitation. The 13 sample sites, seven sites from ROZ and six sites from CDZ, were selected from the ROZ to the CDZ to cover the main typical vegetation types of this area .At each site, three representative plots (20 m × 20 m) were established as replicates. Vegetation communities were investigated and recorded. In addition, the geographical coordinates of each plot were recorded using a GPS (GPSmap 629sc). Leaves of dominant plant were collected and stored at 4°C to later determine the plant functional traits, such as leaf thickness (LT). The soil sample of each plot was collected by randomly selecting 20 cores (0–15 cm depth) in each plot that were thereafter well-mixed and homogenized. In total, 39 soil samples were collected in this study. After removing and discarding most leaves and stones with a 2-mm mesh screen, the soil samples were divided into two portions: one portion was used for the physicochemical analysis, and the other portion was stored at −20°C for DNA extraction.Soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) were measured following the procedures described by 2Cr2O7 oxidation method  was measured as the ratio of leaf surface area to dry mass. Leaf dry matter content (LDMC) was calculated as the ratio of leaf dry mass to fresh mass. LT was measured with Digital Vernier Caliper . Leaf carbon content (LCC) was determined using Kn method . Leaf nin method  and ammon method  followin2O. The cycling conditions were 95°C for 5 min; followed by 32 cycles of 95°C for 45 s, 55°C for 50 s, and 72°C for 45 s; and a final extension at 72°C for 10 min. Three replicate amplifications were mixed together, purified using an Agencourt AMPure XP Kit  following the manufacturer’s instructions and quantified using Caliper . Finally, purified amplicons were pooled in equimolar solution and paired-end sequenced (2 × 300) using a Miseq Reagent Kit v3  on an Illumina Miseq platform by Allwegene Technology (Beijing).Soil DNA was extracted from 0.25-g soil samples within 1 week after storage for a week using PowerSoil DNA Isolation Kit  according to the manufacturer’s instructions. The V3-V4 hypervariable regions of bacterial 16S rRNA genes were amplified using the forward primer 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′) , which iThe sequence data were processed using the QIIME packages  and random networks. A Student The dominant phyla and genera of the bacterial community were identified based on a relative abundance of greater than 1% among all samples or in ROZ and CDZ, respectively. Analysis of variance (ANOVA) was performed to evaluate significant differences in dominant phylotypes and α-diversity between ROZ and CDZ using IBM SPSS Statistics for Windows . The data that were non-normal or heterogeneity of variance were log or square root transformed before analyses. Otherwise, we performed non-parametric Kruskal–Wallis tests. Pearson correlation analysis was used to test the relationships between α-diversity and soil and plant properties.BC) was separated into species turnover  and nestedness (dBC–gra) using the “betapart” package of R  , while tage of R . DistancP was <0.05 and the VIF value was <10 using the “packfor” package  . Then, w package . Finally package . In addiAfter removing the archaea and singleton OTUs, the number of bacterial sequences per sample ranged from 18,182 to 82,732, and these were classified as 707 to 2,477 OTUs. When all samples were compared, the OTU table was rarefied to 18,182 sequences per sample with an OTU richness from 582 to 1,955.Nafulsella, Truepera, and Rubrobacter), belonging to Bacteroidetes, Deinococcus–Thermus, and Actinobacteria phyla, were significantly higher in CDZ than those in ROZ habitat. In contrast, both Marinobacter and Marinimicrobium of the phylum Proteobacteria were more abundant in ROZ than in CDZ habitat , whereas bacterial nestedness showed no significant relationship with spatial distance  rather than nestedness 1.7%, . MoreoveP < 0.0001; P < 0.05; P < 0.001, P > 0.05, The results from the Mantel test indicated that plant community dissimilarity and soil property distance were strongly related to the bacterial β-diversity and species turnover (P = 0.002), but this was not true for the species turnover component . Nevertheless, plant functional traits could not significantly explain variations in bacterial β-diversity (P = 0.114) or species turnover (P = 0.154). For these two models, the fractions representing shared effects were larger and explained 8.2 and 7.4% of the variations of bacterial β-diversity and species turnover, respectively  . RevealiThis study confirmed that bacterial community composition was significantly altered from the ROZ to the CDZ, which was attributed to the species turnover rather than species nestedness in an arid region. This result was significantly determined by the heterogeneity of soil properties and plant community factors due to supplementary water from an inland river; plant leaf functional traits explained less of the variations of the bacterial community composition. In addition, the bacteria network became more stable and complex from the CDZ to the ROZ. Our study enhances the understanding of the relationships between bacterial community composition and water resources-induced environmental changes owing to the lower reaches of the Heihe River. In addition, this study provides a scientific basis for predicting responses of microbial communities to future climate change in arid land. Future studies are necessary to investigate the influence of inland river on temporal dynamics of microbial communities and their networks due to seasonal dynamics of river water.SRP200254.The datasets generated for this study can be found in the SRA of NCBI database under accession number WW, JL, and JW designed the study. WW, ZY, and TZ performed the field investigation and collected the data. WW and JW conducted the statistical analysis. WW and LQ wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Parkinson’s disease (PD) patients frequently develop cognitive impairment. There is a need for brief clinical assessments identifying PD patients at high risk of progressing to dementia. In this study, we look into predicting dementia in PD and underlying structural and functional correlates to cognitive decline in PD. We included 175 patients with PD, 30 with PD dementia, 51 neurologically healthy controls and 121 patients with Alzheimer’s disease (AD) from Skane University Hospital, BIOFINDER cohorts. All underwent cognitive tests, including MMSE, 10-word list delayed recall (ADAS-cog), A Quick Test of cognitive speed (AQT), Letter S fluency, Clock Drawing Test (CDT) and pentagon copying. In non-demented patients with PD, abnormal AQT and CDT results predicted an increased risk of subsequent development of dementia (hazard ratio 2.2 for both). When comparing the cognitive profile between PD and AD, decreased performance on AQT, which measures attention and processing speed, was more typical in PD. Lastly, we investigated the underlying structural and functional correlates for the PD-specific test AQT with magnetic resonance imaging. In PD patients, decreased performance on AQT was associated with i) cortical thinning in temporoparietal regions, ii) changes in diffusion MRI, especially in the cingulum tract, and iii) decreased functional connectivity in posterior brain networks. The overall prevalence of PD is approximately 1% of the population >60 years of age. It has been estimated that 75% of patients with PD that survive more than 10 years develop dementia with a point prevalence of close to 30%2, although some studies suggest a lower cumulative prevalence of 46%3. The development of dementia is a serious problem that severely worsen the quality of life of patients and their relatives4 and increases the costs for society5.Parkinson´s disease (PD) is the second most common neurodegenerative disease6, but also for treating the patients correctly. Randomized clinical trials (RCT) have shown positive cognitive effects of acetylcholinesterase inhibitors7 and memantine8 in PDD. Prediction of PDD may in the future also be valuable for choosing and evaluating neuromodulatory treatments.Accurately predicting and diagnosing PD with dementia (PDD) is not only important for allocating necessary resources and care-giver support9 as well as impaired pentagon copying10. However, perhaps due to underlying heterogeneous pathologies, predicting dementia in PD remains a challenge, and the underlying pathology needs to be more elucidated.Previously, memory and executive impairment has been shown to predict dementia in PDThe three aims of this study were to (1) evaluate the performance of various brief cognitive tests for predicting subsequent development of PD dementia, (2) identify tests specific to the cognitive decline in PD, and (3) examine the underlying brain correlates of the cognitive deficits in PD. First, we studied which cognitive tests could predict development of dementia in patients with PD (n = 175) during a mean of 5.5 (SD 2.3) years of follow-up. Next, patients with PDD at baseline (n = 30) were compared to an independent cohort of AD patients (n = 121) to evaluate which tests were specific for PDD.Finally, in an attempt to shed light on specific patterns of pathology in PD dementia. we evaluated the underlying functional and structural correlates of declining processing speed in PD with magnetic resonance imaging (MRI) modalities, evaluating cortical thickness, white matter tracts and functional connectivity.markers For Identifying Neurodegenerative Disorders Early and Reliably) described in previous publications from our group11 and at http://biofinder.se. This study started in 2008 and is still ongoing. The PD patients met the National Institute of Neurological Disorders and Stroke Diagnostics Criteria for PD12. The patients with PDD met the clinical diagnostic criteria for dementia associated with PD according to Emre et al.13. The healthy controls (HC) served as a control group and were neurologically and cognitively healthy. The AD patients met the Diagnostic and Statistical Manual of Mental Disorders (Third Edition Revised) criteria for dementia and the criteria for probable AD defined by the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer´s and Related Disorders Society14.All participants were recruited at Skane University Hospital in southern Sweden and included patients with PD, PDD, AD as well as neurologically healthy controls. The cases are part of the prospective and longitudinal Swedish BioFINDER study . Conversion to dementia was evaluated at every time point , or by chart review in the case of dropouts. A physician with experience in movement disorders performed a detailed history and neurological exam at every visit, as well as a review of the patient’s charts. A research nurse performed the cognitive testing. In this study, MMSE was chosen as the measure of global cognitive function. To measure episodic memory, the 10-word list delayed recall from the Alzheimer´s Disease Assessment Scale – cognition (ADAS-cog) was used17. Phonemic fluency was measured with Letter S fluency18 and visuospatial function with the Clock Drawing Test (Shulman’s scoring method19) and pentagon copying20, scored using the scoring method suggested by Caffara et al.21. Attention and processing speed was measured with AQT24. Briefly, AQT is a time trial of naming forms, colours and lastly the combination of coloured geometrical figures. The test aims to measure processing speed and executive function. It is a time trial where the figures are described by form and colour first separately, then in combination. The measured variable is the time it takes to name of 40 colors/forms. Motor function was assessed using UPDRS-III25. All patients were examined in the ON-state. For the longitudinal analysis of clinical progression in the PD cohort, all PD patients (n = 175) that were non-demented at baseline were followed clinically for up to ten years, . A physician with experience in neurodegenerative disorders evaluated the patients for presence of PDD at baseline or conversion to PDD at every follow up visit, based on clinical test scores and global functioning at home and in society. For dropouts, patients’ medical records were also reviewed for information about conversion to PDD during the follow up period. In the longitudinal analysis, a predefined AQT Color Form cutoff of >80 seconds was used (+2 SD above mean in a reference population26) and a predefined cutoff of <4 points was used for the Clock Drawing Test19.The study participants underwent cognitive evaluation according to the proposed algorithm and instruments for establishing the diagnosis of PDD at Level I by Dubois 3. The resting state-fMRI protocol comprised 256 T2*-weighted echo planar imaging volumes sensitized to the BOLD effect . Subjects were instructed to lie still with their eyes closed, without thinking of anything in particular and without falling asleep. DKI data comprised 99 volumes with 52 continuous slices, three volumes with b = 0 s/mm2 and 96 volumes with b-values of 250, 500, 1000, and 2750 s/mm2, distributed over 6, 6, 20, and 64 directions27. The voxel size was 2.3 × 2.3 × 2.3 mm3 and FOV 294 × 294 × 120 mm3. Motion and eddy current distortions were corrected using an extrapolation-based method for improved high b-value performance. Image volumes were smoothed using an isotropic 3D Gaussian kernel with a full-width at half maximum of 2.3 mm30. DKI parameter maps of fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD)31 were obtained using in-house developed software which fitted the diffusion and kurtosis tensors by nonlinear optimization.For investigation of regional brain changes associated with cognitive impairment, we performed MR imaging in 108 cases with PD and 17 cases with PDD. The clinical information closest to the MRI scan was analysed. All MR imaging was performed on 3 T Siemens Skyra MR equipment using the standard 20 channel headcoil. Coronal MPRAGE comprised 180 slices, voxel size 1 × 1 × 1 mmhttp://surfer.nmr.mgh.harvard.edu, we analysed T1MPRAGE MR images.Using the free image analysing tool Freesurfer, available at 32. Non-brain tissue is removed and grey and white matter is separated. The post processing results were quality controlled with visual inspection prior to analysis. Three patients were excluded due to corrupted data. Using the QDEC application in the free Freesurfer software package, we correlated the AQT scores, as well as the results from the other cognitive tests , to cortical thickness as the dependent variable in a whole brain analysis, using Monte Carlo simulation of 1.3. The model was balanced for age and years of education.Freesurfer software package is a validated tool for measuring cortical thickness33. One seed-ROI covering the full extent of the specific tract and logical AND- and NOT-ROIs were defined in Montreal Neurological Institute (MNI) 152 standard-space and warped back to native-space utilizing the warp-fields generated by FLIRT and FNIRT34. All tracts were visually inspected and ROIs were adjusted in order to secure location of the tract in the intended anatomical region with the number of streamlines below 100. The average parameter estimate for each tract was extracted and used in the subsequent analyses. Since the differences we aimed to elucidate could be relatively subtle, we abstained from a whole brain approach WBA, because of the risk of it being to insensitive considering the multiple comparison issues with such voxel-based approach.Tractography using deterministic tracking based on constrained spherical deconvolution (CSD) was performed to generate the left and right dorsal cingulum (CG), the hippocampal CG, the corticospinal tract (CST) the fornix (FX), the uncinate fasciculus (UF), the superior longitudinal fasciculus (SLF), the inferior longitudinal fasciculus (ILF), and the inferior fronto-occipital fasciculus (IFO)http://www.fil.ion.ucl.ac.uk/spm). In brief, the following steps were applied: removal of first six volumes, correction for time offsets between slices, realignment, normalization to the MNI template, temporal high-pass filtering (0.01–0.08 Hz), regression of six head motion parameters, and white matter and cerebrospinal fluid signals. To remove the influence of volumes affected by motion, we applied a scrubbing procedure using root-mean-square intensity differences between volumes N to volumes N + 1 with DVARS35. In this study, 1 PD and 2 PDD patients showed head motion > =3 mm translation and rotation, and for this reason they were excluded from the analyses.fMRI scans were preprocessed using the Statistical Parametric Mapping (SPM8) software  software package37.Functional brain networks were constructed for each subject using a set of nodes representing brain regions. These nodes were connected by edges representing the statistical interdependence in blood oxygen level-dependent (BOLD) signals. The nodes were defined using the 200 cortical and subcortical regions provided by the Craddock atlas38. For comparisons between the different diagnostic groups at baseline , a univariate linear model corrected for age, sex, years of education and disease duration was applied with corrections for multiple comparisons using the Bonferroni method. The PD and AD patients were compared with a similar model, corrected for MMSE and age as a measure of global cognitive function and disease burden. For correlation analysis between cognitive tests and tractography, partial Pearson correlations were performed, corrected for age, sex, years of education, disease duration and brain volume. Since these analyses were exploratory in nature, we did not correct for multiple comparisons. For evaluation of the predictive value of cognitive tests on subsequent development of dementia, we performed cox regression analysis corrected for age, sex, disease duration and years of education.SPSS 25 was used for all statistical analysis except for the Freesurfer data where the built in application Query Estimated Design Contrast, QDEC, was usedIn Freesurfer, the built-in application QDEC was used for analysis. The analysis was adjusted for age, sex, years of education and disease duration. A Monte Carlo simulation was used and p < 0.05 was considered significant. For the functional network analysis, a regression analysis was carried out to test whether AQT scores correlated with functional connectivity in the whole patient group, while controlling for age, sex, education and disease duration. Correction for multiple comparisons was performed using a false discovery rate (FDR) of 0.001 (1000 permutations).All individuals gave their written informed consent. The study procedure was approved by the local ethics committee at Lund University, Sweden, and conducted according to the Helsinki Declaration.The baseline demographics of the participants are presented in Table Next, we studied which cognitive tests could predict subsequent development of dementia in non-demented PD patients. The patients were followed between 1.5 and 10 years with an average of 5.5 SD 2.3) years. In total 41 (23%) PD patients converted to dementia during follow-up. We applied a Cox regression model corrected for age, sex, disease duration and years of education. Z-scores of the results on the cognitive tests were used for comparability and the results are presented in Table .3 years.Next, we compared the cognitive profile of our PDD patients to that of an independent AD cohort to evaluate if any of the two predictive tests with very similar predictive power (AQT and Clock Drawing Test) were more specific to PD. The model was corrected for age and MMSE as proxy of disease severity. We found that the PD patients exhibited slower processing speed on all AQT tasks whereas there was no difference on the Clock Drawing Test Table . As expeSince our results suggested that AQT not only predicted conversion to PDD in PD but also seems more typical to PD than AD, we investigated the underlying structural and functional correlates to this seemingly PD specific test in an attempt to elucidate underlying pathological patterns. This was done by analysing associations between processing speed (measured with AQT Color Form) and cortical thickness, white matter tracts and functional networks in patients with PD or PDD who underwent MRI n = 125). Clinical data for this subset of the cohort is presented in a supplement . When comparing cognitive profiles between PDD and AD, AQT was more specific to PDD, and we therefore further examined the underlying structural correlates of AQT performance (i.e. declining processing speed) in PD and PDD. We found that poor performance on AQT was associated with cortical atrophy in temporoparietal areas (mainly in the left hemisphere), diffusion changes in certain white matter tracts, mainly the cingulum tract, and decreased connectivity in posterior networks.et al. found evidence that both verbal fluency, with a relative risk (RR) of 9.4 (95% CI 1.9–47.5) for a global decline, and inaccurate pentagon copying, with a RR of 5.2 (95% CI 1.9–14.1), predicted dementia in PD39. This could not be replicated in our cohort 47, as well as degeneration of other neuronal cell types in the cortex. Previous studies of cortical thickness in different stages of PD have shown correlations between disease stage and cortical thinning48. For example, Zarei et al. found correlations between decreased MMSE and cortical thinning in several cortical areas in PD, including the posterior cingulate and precuneus. In our study, worse AQT scores correlated to decreased thickness in the posterior cingulate cortex as well as in the inferior parietal and temporal areas.The neuropathology of PDD is complex. Likely, a combination of α-synuclein pathology and AD-like pathology (amyloid-β and tau aggregates) is responsible for the cognitive symptomset al. found decreased FA values in the cingulum tract in PD and PDD49. In our study we also found weak correlations between decreased processing speed and increased MD values in the cingulum as well as in the ILF and IFO tracts connecting the occipital lobe to the temporal lobe and frontal lobe, respectively50. While other studies have found evidence of frontostriatal involvement in dysexecutive syndromes in PD51, we also found associations between AQT and decreased connectivity in posterior networks and tracts with occipital connections. This could represent deficits in rudimentary visual processing and set-shifting, which is in agreement with the brain regions that are involved during the AQT Color Form task as visualized using fMRI and regional cerebral blood flow measures (rCBF)52.When it comes to structural connectivity, Kamagata 15 and even some additional ones. The structural MRI findings were considered exploratory in nature, and therefore not corrected for multiple comparisons. Further studies are therefore warranted to confirm these findings. Strengths of the study are the relatively large and well described cohort, the large proportion of the original cohort that underwent MRI, and the long and structured follow-up.A limitation of the study is that a larger battery might have identified other predictive tests for dementia conversion in PD, but, nonetheless, our cognitive battery included all tests recommended by the Movement Disorder Society Task ForceWe conclude that the dementia in PD is predominantly characterized by declining processing speed and executive dysfunction, which can be evaluated with the readily available test AQT. AQT can also predict subsequent development of dementia. Using the predefined cutoff of >80 seconds for AQT Color Form, clinicians can identify PD patients who have increased risk of developing dementia. Declining processing speed seems to have structural correlates in the cingulum tract and posterior functional networks. Further studies are needed to develop more precise methods and markers to predict cognitive decline in PD, and to shed more light on the underlying structural basis of cognitive decline in PD.Dataset1


The detection accuracy was not ideal when the soil particle sizes were too big (1–2 mm) or too small (0–0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was indicated that the larger the gap between soil particle sizes, the worse the accuracy of soil nitrogen detection. In conclusion, soil nitrogen detection precision was affected by soil particle sizes to a large extent. It is of great significance to optimize the pre-treatments of soil samples to realize rapid and accurate detection by NIR spectroscopy.Soil nitrogen is the key parameter supporting plant growth and development; it is also the material basis of plant growth. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near-infrared (NIR) spectroscopy is widely used for rapid detection of soil nutrients. In this study, the variation law of soil NIR reflectivity spectra with soil particle sizes was studied. Moreover, in order to precisely study the effect of particle size on soil nitrogen detection by NIR, four different spectra preprocessing methods and five different chemometric modeling methods were used to analyze the soil NIR spectra. The results showed that the smaller the soil particle sizes, the stronger the soil NIR reflectivity spectra. Besides, when the soil particle sizes ranged 0.18–0.28 mm, the soil nitrogen prediction accuracy was the best based on the partial least squares (PLS) model with the highest Rp As the main source of nutrient supply for plant growth, the nutritional status of soil is one of the key factors directly influencing plant growth and development . During 2 > 0.90) for soil organic carbon, Kjeldahl nitrogen, soil moisture, cation exchange capacity, microbial biomass carbon, basal soil respiration, acid phosphatase activity and β-glucosidase activity [Firstly, NIR spectroscopy could be used as a rapid, inexpensive and non-destructive technique to predict the physical, chemical and biochemical properties of soil. The predicted results were evaluated as excellent  systematically study the effect of soil particle sizes on the detection of soil nitrogen by NIR spectroscopy; (2) conduct a qualitative and quantitative analysis of mixed spectra based on different soil particle sizes; (3) model and analyze the soil NIR spectra by four spectral pretreatment methods and five modeling methods, attempting to achieve high feasibility and reliability.The soil type, color, and other physicochemical properties will affect the spectral characteristics of soil to a large extent. Therefore, before exploring the effect of soil particle sizes on soil nitrogen detection using NIR spectroscopy, the spectral properties of the red soil used in this experiment were analyzed. The original NIR spectrum of soil is given in According to In this paper, the average reflectivity of soil NIR spectra with different particle sizes was collected and the corresponding reflectivity curves are presented in According to Considering the spectral information overlap and noise on the edge of acquisition band, the 930-1670 nm soil NIR spectra of six soil groups with different soil particle sizes were pretreated with Savitzky–Golay (S–G) smoothing, detrend (DT), standard normal variation (SNV), and first derivative (1st-Der), respectively, and then modeled by partial least squares (PLS), competitive adaptive reweighted sampling-partial least squares (CARS-PLS), backward interval partial least squares (biPLS), genetic algorithm-partial least squares (GA-PLS) and successive projections algorithm-partial least squares (SPA-PLS), respectively. The sample set portioning based on the joint x–y distance (SPXY) method was used to separate the soil samples into a calibration set and validation set at a ratio of 2:1 for each soil group. The modeling results of PLS with different pretreatments are presented in As illustrated in 2 < 0.893) when the soil particle sizes were 1–2 mm. It was shown that large soil particle sizes were averse to soil nitrogen detection and this conclusion is consistent with Cozzolino’s and Bao’s research [2 < 0.885; 0.28–0.45 mm: 0.809 < Rp2 < 0.944) improved greatly. Additionally, when the soil particle sizes ranged 0.18–0.28 mm, soil nitrogen prediction achieved the best accuracy with the highest Rp2 of 0.983. However, soil particle sizes that were too small (less than 0.18 mm) were not helpful for the improvement of detection accuracy (0.824 < Rp2 < 0.926). Compared with the five soil groups, Rp2 reached 0.8–0.9 when the soil particle sizes were in the range of 0–2 mm, which indicated that the detection of soil nitrogen contents using NIR was also affected by the uniformity of soil particle sizes. Therefore, the prediction effect of six soil particle size ranges can be ranked as follows: 0.18–0.28 mm > 0–0.18 mm > 0.28–0.45 mm > 0.45–1 mm > 0–2 mm > 1–2 mm.First, from the perspective of the effect of soil particle sizes on the detection of soil nitrogen contents by NIR, the detection results were relatively poor . The reason could be that MSC and SNV could efficiently eliminate the influence of soil particle sizes, surface scattered light, and optical path change on NIR spectroscopy, which improved the spectral resolution, reduced the standard deviation between samples and separated the main characteristic peaks for quantitative analysis . It can 2, the better the model stability. No matter which method was used, the RPD of prediction models were more than 4 (> 3) mostly when the soil particle sizes were in the range of 0.18–0.28 mm, which satisfied the agriculture applications [Finally, from the perspective of the model stability, it can be seen that the higher the Rpications . 2 and RPD.In summary, for the analysis of soil NIR spectra with different soil particle sizes, there were some differences in model prediction results using different pretreatment methods and modeling methods. However, when the soil particle sizes were 0.18–0.28 mm, soil nitrogen prediction accuracy achieved the highest RpIn order to further explore the influence of soil mixing spectra with different particle sizes on soil nitrogen detection, the spectra of five different soil particle size ranges  were mixed with each other in equal proportions and modeled by PLS. We obtained five average spectra (108 samples of each average spectra) of different soil particle sizes before exploring the modeling effects of different mixing spectra .2 and RPD of PLS model is given in As shown in 2 and RPD of individual soil group were larger than those of two mixed soil groups. Moreover, the PLS modeling effect of two mixing spectra with small differences in soil particle sizes were better than those of two mixing spectra with big gap in soil particle sizes. A good explanation was that soil NIR reflectivity was affected by soil particle sizes, and the difference of soil particle sizes resulted in worse uniformity of soil groups to be detected, which would reduce the model accuracy. The results showed that the larger difference of mixing spectra with soil particle sizes, the worse the modeling accuracy. Therefore, the consistency and uniformity of soil particle size should be maintained as far as possible to achieve high accuracy when using NIR spectroscopy to detect soil nitrogen content.Clearly, the RpThe experimental soil was collected from Maoming city, Guangdong province, China . The portable NIR optical instrument from Isuzu Optics Corp  is an interferometer instrument reflective with two integrated tungsten halogen lamps. This instrument collects spectral information in the range of 900–1700 nm, with an optical resolution of 10 nm. The preparation process of soil samples was as follows: First, the soil samples were dried by air and sieved with a 2 mm mesh sieve. Second, soil nitrogen concentration was detected by biuret method and Kjeldahl determination. Third, one group of soil samples with soil particle sizes in the range 0–2 mm was obtained. The other five groups of soils with different particle sizes were obtained as follows: (1) The soil samples (0–2 mm) were sieved with a 0.18 mm sieve and the soil particle sizes in the range 0–0.18 mm were obtained. (2) The remaining soil sieved through a 0.18 mm sieve was sieved again with 0.28 mm sieve and the soil particle sizes ranging 0.18–0.28 mm were obtained. (3) The remaining soil sieved through a 0.28 mm sieve was sieved again with 0.45 mm sieve and the soil particle sizes ranging 0.28–0.45 mm were obtained. (4) The remaining soil sieved through a 0.45 mm sieve was sieved again with 1 mm sieve and the soil particle sizes ranging 0.45–1 mm were obtained. (5). The remaining soil sieved through a 1 mm sieve (1–2 mm) was obtained. Thus, six soil sample groups with different particle sizes ranging (a) 1–2 mm; (b) 0.45–1 mm; (c) 0.28–0.45 mm; (d) 0.18–0.28 mm; (e) 0–0.18 mm; and (f) 0–2 mm were obtained. The urea solutions with different concentrations were mixed with the soil samples and eight gradients with different soil nitrogen concentrations were obtained . Finally, the soil samples were dried by air and pressed into 10 mm × 10 mm × 2 mm blocks. There were 108 samples (12 samples for each concentration) in each group, and 648 samples in total.Before performing the soil NIR spectra measurement, the instrument was preheated for 15 min and prepared with blackboard and whiteboard correction operation. In order to maintain the integrity of the original soil spectra as well as achieve the rapidity during the detection process, the spectral acquisition parameter is set up as 400 points, and each spectrum is obtained by averaging three scans. When the soil spectra were measured, the samples were placed on the light source window, which avoided the phenomenon of light leakage since the size of soil sample is larger than that of light source window.NIR light is an electromagnetic wave between the infrared and visible light whose wavelength range is from 780 nm to 2526 nm. The spectral information originates from the vibration of the O-H, C-H and N-H groups containing hydrogen internal vibration frequency and sum frequency overlap, which can reflect the variation of organic matter in the characteristic spectral region . AccordiIn order to achieve a better model prediction effect, in this study four spectral pretreatment methods were applied to preprocess the original soil NIR spectra, that is, S-G smoothing, DT, SNV and 1st-Der. Among them, S-G smoothing is widely used to remove noise from original spectrum such as remove high frequency noise . DT algoPLS is a common-used regression modeling method for analyzing spectral data based on its flexibility and reliability in dealing with the redundant spectral data . During CARS is a variable selection method based on the principle of “survival of the fittest” , which uBiPLS is a variable selection method based on the PLS modeling method, it aims to filter the wavelength range of PLS model and reduce the number of sub-intervals of the worst or collinear variables, which select the best principal component number according to RMSECV . For selSPA is a forward variable selection method, which uses vector projection analysis to find the variable group with minimal redundancy information to effectively eliminate the collinear, singular and instable variables in the spectra. Araujo et al.  first prThe aim of genetic algorithm (GA) is to search for the optimal solution by simulating the natural evolution process . The bigc and Rp represent the correlation coefficient of calibration set and prediction set, respectively, and RMSEC and RMSEP represent the root mean square error of the calibration set and prediction set respectively. Besides, RPD was suggested to be at least 3 for agriculture applications; 2 < RPD < 3 indicates a model with a good prediction ability; 1.4 < RPD < 2 is an intermediate model requiring some improvement; and RPD < 1.4 indicates a poor model prediction ability [In this experiment, the modeling effect was evaluated by the correlation coefficient R, the root mean square error (RMSE) and the residual predictive deviation (RPD). The correlation coefficient R reflects the level of intimacy between variables, RMSE reflects the model accuracy, and RPD reflects the model prediction ability. The higher the R and RPD and the lower the RMSE, the better the prediction model performance. In this paper, R ability . In addi2 of 0.983, the RPD of 6.706. The detection accuracy was not ideal when soil particle sizes were too large (1–2 mm) or small (0–0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was shown that the larger the difference of soil particle size, the worse the soil nitrogen detection accuracy. In conclusion, soil nitrogen detection precision was affected by soil particle size to a large extent. It is of great significance to optimize the pretreatments of soil samples to realize rapid and accurate detection of soil nitrogen by NIR spectroscopy.In this paper, the variation law of soil NIR spectra with soil particle sizes was studied. The results showed that the smaller the soil particle sizes, the stronger the reflectivity of soil NIR spectra. When the soil particle sizes ranged 0.18–0.28 mm, soil nitrogen prediction achieved the best accuracy based on PLS model with the highest Rp


To identify in patients with dry mouth the effects of a novel test agent  versus a control agent  versus no treatment on dry mouth, plaque, salivary pH and buffering capacity, gingival health, and tooth sensitivity.  In this cross-over study, ten subjects with dry mouth used test and control dry mouth interventions, as well as no dry mouth intervention in randomized sequence. Plaque Index, Gingival Index, Sulcus Bleeding Index, Plaque staining, and photographs were recorded at baseline and end of each study arm. Salivary volume, pH, and buffering capacity were also recorded at these time points. Additionally, subjects completed a questionnaire for dry mouth and dentinal sensitivity at each visit. p < 0.05). Saliva volume and pH buffering improved significantly after use of test and control products (p < 0.05).  Reductions in plaque presence and clinical indices were similar after use of test or control products ( The effects of a novel dry mouth intervention are similar to those of an existing OTC remedy and are significantly better than no intervention. With a reported prevalence of 5% to 46% , dry mouDry mouth symptoms range from mild discomfort to considerable oral disease that may impact oral health and even the patient's quality of life . Oral anTreatment for dry mouth typically takes 2 forms: 1) addressing etiological factors and (2) alleviating symptoms and/or increasing salivary flow  addressi. SystemiThe goal of this clinical study was to identify, in patients with dry mouth, the effects of (1) a novel test agent  versus (2) a control agent that is commonly used by individuals with dry mouth , and (3) no treatment on plaque presence, salivary pH and buffering capacity, gingival health and tooth sensitivity, as well as intraoral comfort and function. These goals were formulated to test the hypothesis that the novel mouthwash has similar effects on these variables as one of the leading dry mouth agents, Biotène Dry Mouth Oral Rinse.This study was performed in full compliance with University of California IRB-approved protocol 2013–9778. All work was conducted in accordance with the Declaration of Helsinki (1964), with the human subjects' understanding and written consent. Ten subjects with confirmed dry mouth participated in this study. They were recruited at the University of California, Irvine by word-of-mouth, flyers, and e-mail recruitment. All subjects signed an informed consent at the beginning of the study, as well as statement of patient rights and photographic release forms. Subjects received an incentive payment at study completion.(1) Males and female subjects of all races and ethnicities, age between 18 and 75 years old(2) Demonstrated an unstimulated whole saliva flow rate below 0.2 ml per minute and a stimulated saliva flow rate less than 0.5 ml in 5 minutes(3) At least 5 natural teeth present in each quadrant (excluding third molars)  Inclusion criteria:(1) Participation in any other clinical study involving the mouth or xerostomia within the last 30 days prior to enrolment into this study.(2) Pregnant or nursing women (self-reported).(3) Subjects who were unable to defer dental treatment during the study dates.(4) History of significant adverse effects following use of oral hygiene products such as toothpastes and mouth rinses or allergy to personal care/consumer products or their ingredients.(5) Significant past unresolved or current medical problem history.(6) Presence of other major pathologies, such as herpetic infection, major recurrent apthous ulcers, or other ulcer forming diseases, abscesses, granulomas, or severe gingivitis, which might compromise the ability to perform measurements.(7) Other significant disease or disorders that, in the investigator's opinion, would exclude the subject from the study including systemic conditions that would influence the course of periodontal disease.(8) Active acquired immunodeficiency syndrome (AIDS) or hepatitis B/C (self-reported).(9) Smokers.(10) Self-reported GERD condition.(11) Presence of any condition, abnormality or situation at baseline that in the opinion of the Principal Investigator night preclude the volunteer's ability to comply with study requirements, including completion of the study or the quality of the data.  Exclusion criteria:(12) Subjects would not be allowed to receive dental treatment (except emergency treatment) during the study.(13) Subjects would be asked to refrain from all nonstudy oral hygiene procedures other than their usual brushing, flossing, and mouth washing routine during the study.(14) Subjects embarking on a course of medication during the study would have to inform the Study Coordinator so that a decision could be made as to whether they could continue in the study. A five-minute unstimulated saliva test might be taken to verify no change in saliva flow resulting from use of the new medication. Compliance with the protocol was checked at each test visit and recorded on the appropriate documentation.  Subject restrictions:https://www.randomizer.org/). Test and control formulations were provided in numbered plain white containers to blind subjects and investigators with regard to treatment allocation. For the “no treatment” category, subjects were asked to refrain from any symptomatic dry mouth treatment; therefore, in this section of the study, the patient was not blinded. However, the clinical evaluator remained blinded as to the treatment allocation. Subjects were provided with a new standard Oral B ProFlex toothbrush  for each arm of the study and instructed to continue with their usual oral hygiene measures.The study had 3 arms, whereby in two arms, subjects used a test or control dry mouth intervention; in the third arm, they used no intervention for xerostomia . SubjectAfter obtaining informed written consent during the baseline (Day 0) visit, standardized photographs as well as full-mouth Plaque Index (PI) , GingivaOral Essentials Hydrating Formula Mouthwash, Beverly Hills, CA, USA. Use approximately one tablespoon, swish vigorously for 60 seconds, and then spit out. Use up to 3 times per day.Biotène Dry Mouth Oral Rinse was obtained from GlaxoSmithKline Consumer Healthcare L.P., Moon Township, PA, USA. Use approximately one tablespoon, rinse for 30 seconds, and then spit out. Use up to 5 times per day.Image J software (https://imagej.nih.gov/ij/) was used to process the digital intraoral photographs of the buccal/labial surface of each tooth, subject, and timepoint. This was achieved using the standard technique, whereby the borders of all plaque accumulations are visually delineated, and the areas thus mapped are expressed as percent coverage of each tooth surface. Digital image analysis also evaluated plaque age at each location, based on stain color .t-statistic. A two-sample t-test was also performed on the differences in the changes in each arm to see whether one treatment was more effective than the other.The value for each clinical index for each tooth unit at the end of each washout period was used as the baseline value for the subsequent arm of the study. The effects of each dentifrice on each clinical index and on plaque presence were tested using sums and differences of changes between study arms, calculated for each subject. Means and standard deviations were calculated for each group. The sums and differences were tested for significance by means of a two-sample All subjects completed a routine self-evaluation questionnaire for dentinal sensitivity, dry mouth status, and mouthwash ease of use at every visit .p < 0.01) improvement during the test and control agent use arms of the study except for “oral comfort at waking in the morning” (significant effect only for test intervention) and “do you sip liquids to aid swallowing” (no significant change from baseline for test and control interventions).Clinical criteria showed outstanding and highly significant Percent of total tooth surface covered by plaqueSurface plaque was quantified three times: at baseline, and after each 7-day study arm, respectively . Plaque Using Image J software, % of surface coverage was quantified for old (>24 h) and new (<24 h) plaque for each study arm.p < 0.05). Old and new plaque levels were similar for the test and the control products (p > 0.1). Old plaque presence fell to approximately 50% of the baseline value, while new plaque presence was reduced to approximately 60% of the baseline level. Typically, greater saliva presence and flow are associated with better lubrication and self-cleansing of the mouth [There was a significant reduction in old and new plaque presence after use of test or control products for 1 week versus no product  (PI), Gingival Inflammation  (GI), and Gingival Bleeding (mSBI) .p > 0.1). There was a significant reduction in clinical Plaque Index by approx. 60% after use of test or control products for 1 week (p < 0.05). This parallels the reduction in clinical plaque presence that was quantified using photographs and image quantification software (see previous paragraph). The reduction in Gingival Index after 7 days of test or control product use almost reached a significant level . A lower Gingival Index is generally seen as an indication of better gingival health. This finding may be related to the reduction in plaque levels, which are typically related to an improvement in gingival health [Generally, clinical indices were similar for the test and the control products  (p > 0.1). The transient increase in saliva production is similar to that reported in other studies using similar products [The volume of saliva collected over 5 minutes in subjects with xerostomia was approximately doubled at the end of the test and control study arms, representing a statistically significant increase in each case ( < 0.05) . In the products , 34.p < 0.05)  .p < 0.05). No change was observed in the arm when no intervention was used (p > 0.1)  .Overall, this pilot study demonstrated that a novel dry mouth intervention has similar effects on plaque presence, salivary pH and buffering capacity, gingival health, and tooth sensitivity, as well as intraoral comfort and function as those of an existing OTC remedy –37. The In this first pilot study, the hypothesis was confirmed that a novel dry mouth intervention has similar effects as those of an existing OTC remedy and is significantly better than no intervention. Further studies are required in more patients and over a longer period of time.