Cerebral subcortical atrophy occurs in both Alzheimer’s disease (AD) andfrontotemporal dementia (FTD) but its significance for clinical manifestationsand differential diagnosis between these common types of dementia has not beenextensively investigated.To compare the severity of cerebral subcortical atrophy in FTD and AD and toanalyze the correlations between cerebral subcortical atrophy anddemographics and clinical characteristics.Twenty three patients with FTD and 21 with AD formed the sample, whichcomprised 22 men and 22 women, aged 33 to 89, with mean age (±SD) of68.52±12.08 years, with schooling ranging from 1 to 20 years, with amean (±SD) of 7.35±5.54 years, and disease duration with amean (±SD) of 3.66±3.44 years. The degree of cerebralsubcortical atrophy was measured indirectly with a linear measurement ofsubcortical atrophy, the Bifrontal Index (BFI), using magnetic resonanceimaging. We evaluated cognition, activities of daily living and dementiaseverity with the Mini-Mental State Examination, Functional ActivitiesQuestionnaire and the Clinical Dementia Rating, respectively.There was no significant difference (p>0.05) in BFI between FTD and AD.The severity of cognitive deficits (for both FTD and AD groups) and level ofdaily living activities (only for AD group) were correlated with BFI.A linear measurement of cerebral subcortical atrophy did not differentiate ADfrom FTD in this sample. Cognitive function (in both FTD and AD groups) andcapacity for independent living (only in AD group) were inversely correlatedwith cerebral subcortical atrophy. Measures of hippocampal atrophy have proven the most sensitive way ofdifferentiating mild to moderate Alzheimer’s disease from non-demented elderly. Of thesemeasures, the width of the temporal horn yields the highest sensitivity, predicting thedisease in 73% of cases with 95% specificity.5Diagnosis in life, of Alzheimer’s disease (AD) and Frontotemporal dementia (FTD) is madeon clinical grounds, but currently used criteria are burdened with considerablesubjective judgments,11Differentiation between FTD and AD on neuroimaging, however, remains a great challenge,especially in the clinical setting.12 Sensitiveimaging providing linear and volumetric measures of atrophy rates have been proposed totrack this decline.17 Generally these measures are larger in patients withdementia than in healthy elderly.18Cerebral atrophy occurs in almost all types of dementia and is characterized by a loss ofglobal cerebral volume that can be indirectly observed by ventricular and cerebralsulcal enlargement.In this study, we aimed to better understand the relationship between the severity ofcerebral subcortical atrophy and the type of dementia (FTD and AD), as well as toexplore the relationship of age, duration and aggravation of dementia, educationallevel, daily living activities and cognition, with cerebral subcortical atrophy. Finallywe test the usefulness of a linear measure of atrophy in differentiating AD fromFTD.A total of 44 participants diagnosed with dementia were recruited from theClinicas Hospital at the Federal University of Goiás Medical School(FM-UFG), Brazil. There were no gender or ethnic restrictions. The studyinvolved 22 men and 22 women, aged 33 to 89 years, with mean age (±SD) of68.52±12.08 years, with schooling ranging from 1 to 20 years, with mean(±SD) of 7.35±5.54 years and disease duration with a mean(±SD) of 3.66±3.44 years.20The clinical diagnoses were reached by an experienced psychiatrist/neurologist(LC) based on patient history, neuroimaging results and neuropsychologicaltests. Diagnosis of dementia was based on the criteria of the Diagnostic andStatistical Manual Mental Disorders, Fourth Edition (DSM-IV).21 while thediagnosis of probable AD was based on the National Institute of NeurologicalDisorders and Communicative Disorders and Stroke-Alzheimer´s Disease and RelatedDisorders Association (NINCDS-ADRDA) criteria.22Etiology of dementia included patients with Alzheimer’s disease (n=21) andFrontotemporal Dementia (n=23). Diagnosis of FTD was based on Neary et al.criteriaPrior to carrying out this research, approval by the local research ethicscommittee was obtained (protocol number: 006/05). All subjects who agreed toparticipate signed a written informed consent.24 A gradedcaliper with a 0.1 mm scale was used for this linear measurement on filmcopy.Magnetic resonance was performed on a 1.5–T MRI unit with a quadrature headcoil. T1-weighted sequences were analyzed for this study. From the axialslice of structural neuroimaging (Magnetic Resonance Imaging), the BFI wasmeasured on a plane parallel to the temporal lobe plane at the level of themaximum width between the tips of the frontal horns of the lateralventricles, and defined as the ratio of this value to the diameter of theinner skull table at the same level. The resulting ratio was then multipliedby 100 and expressed as a percentage .15,16,2325The dilatation of the frontal horns of the lateral ventricle is one of theearliest changes seen in cerebral atrophy, while the BFI is a more reliableand practical linear measurement to predict early cerebralatrophy.Clinical Dementia Rating (CDR) – Dementiaseverity was determined by total on the Clinical Dementia Rating Scale. TheCDR assesses cognitive function in six domains: memory, orientation judgmentand problem solving, community affairs and personal care. Based on sixscores, a global CDR score is assigned in which CDR 0 indicates no dementia,CDR 0.5 indicates very mild dementia, CDR 1 indicates mild dementia, CDR 2indicates moderate dementia, and CDR 3 indicates severe dementia.19Mini-Mental State Examination (MMSE) – Allpatients completed the MMSE at baseline, which was administered to determinecognitive function.26Pfeffer Functional Activities Questionnaire(FAQ) – Caregivers of dementia patients completed thisquestionnaire. It is a good instrument for assessing functional status, andincludes ten questions on Activities of Daily Living (ADL).27n=23) and the otherwith AD (n=21). The BFI was compared in both groups for allanalyzed variables.All 44 individuals were assessed using the BFI, CDR, MMSE and FAQ. Thus,duration of dementia and education level (in years) were also examined andserved as inputs for the survival analysis. The neurological examination wasperformed during the same period as the clinical imaging. The patients weredivided into two groups: one with FTD  was performed to compare mean rates ofvariation between the two patient groups. The analyzed variables were: age,duration of dementia, MMSE scores, Functional Scale of Pfeffer’s scores,level of education in years, Clinical Dementia Rating Scale and BFI rate. Weestablished the confidence interval as 95% for the statistical tests.The Spearman Coefficient (rs) was used to obtain the correlation p and toverify the correlations between mean rates of brain atrophy (measured byBFI) and all other variables. The Spearman’s Coefficient was thenon-parametric alternative when the data was not Gaussian and linear.In the FTD group, only the MMSE score showed a strong correlation with BFI . The AD p<0.05). Other correlations were also notsignificant.Age, duration of dementia and educational level were not correlated with BFI ineither patient group . This finding is in line with previous reports in the literature thathave shown distinct types of cerebral changes predicting impaired performance onspecific cognitive tests.38 Activities ofdaily living performance decreased with increased subcortical atrophy only for theAD group. Perhaps, one explanation for this fact is that FTD patients present areduced capacity to perform daily tasks from the early stages of disease (adifference from AD),39 when BFIvalues still remain low.A small number of studies have focused attention on the relationship betweenactivities of daily living and linear brain measures in dementia patients, but onlyin Vascular Dementia and normal aging.We found no correlation between duration of symptoms and the linear measurement ofsubcortical atrophy. This may be expected because the extent of dementia is only anestimate. To our knowledge, no previous study has reported the association involvingduration of dementia and subcortical atrophy measured by BFI.We have also demonstrated that subcortical atrophy is not correlated with educationallevel. This could possibly be explained by the fact that participants had a largediscrepancy in terms of years of education. Clinical pathological studies arenecessary to clarify the association between subcortical atrophy and progression ofdementia.Studies including only one brain variable can be misleading because their putativeassociation may be due to a correlated brain change while cerebral atrophy is anindirect measure of pathological processes occurring on a cellular level. Inaddition, the BFI is a non-specific finding which can result from brain injury ordegeneration and which occurs normally in ageing, although many disease processesresult in distinctive patterns of atrophy due to differential involvement ofspecific areas of the brain.In conclusion, a linear measurement of subcortical atrophy such as BFI probably isnot useful for providing a differential diagnosis between AD and FTD. Furthermore,cognitive function (in both FTD and AD groups) and capacity for independent living(only in AD group) decreased with increased subcortical atrophy. Our findings alsorevealed that age, duration of dementia and educational level do not significantlycorrelate with degree of cerebral atrophy.


Recent studies have shown the potential of food waste materials as low cost adsorbents for the removal of heavy metals and toxic elements from wastewater. However, the adsorption experiments have been performed in heterogeneous conditions, consequently it is difficult to compare the efficiency of the individual adsorbents. In this study, the adsorption capacities of 12 food waste materials were evaluated by comparing the adsorbents’ efficiency for the removal of 23 elements from complex multi-element solutions, maintaining homogeneous experimental conditions. The examined materials resulted to be extremely efficient for the adsorption of many elements from synthetic multi-element solutions as well as from a heavy metal wastewater. The 12 adsorbent surfaces were analyzed by Fourier transform infrared spectroscopy and showed different types and amounts of functional groups, which demonstrated to act as adsorption active sites for various elements. By multivariate statistical computations of the obtained data, the 12 food waste materials were grouped in five clusters characterized by different elements’ removal efficiency which resulted to be in correlation with the specific adsorbents’ chemical structures. Banana peel, watermelon peel and grape waste resulted the least selective and the most efficient food waste materials for the removal of most of the elements. Today, excessive release of wastewater particularly rich in heavy metals from industrial activities is a critical environmental problem worldwide . Many meThere are several physic-chemical methods to remove elements from wastewater such as adsorption, chemical precipitation, solvent extraction, reverse osmosis, ion exchange and chemical reduction ,2,3. AdsDifferent materials with high specific surface areas such as activated carbons, resins and zeolites, have been widely used for wastewater treatment ,5. HowevAmong the low cost materials, food waste adsorbents compete favorably in terms of cost, efficiency and ease of operation . RoughlyRecent studies have shown that banana peel , apple pThe aim of this study is to evaluate the adsorption capacities of 12 food waste materials , comparing their efficiency for the removal of 23 elements (for the most of whom the adsorption capacities have never been assessed) from multi-element solutions (at pH 2.0 and pH 5.5), in homogeneous experimental conditions. The use of multi-element solutions does not allow the identification of the adsorption isotherms of the single elements and the determination of the interaction mechanism for the adsorption, which in part have already been defined in previous studies ,38,39,40The porous surface of the food waste materials is particularly suited to adsorb different elements. In addition, the adsorbent surfaces have various types and amounts of functional groups, which may act as selective adsorption active sites for metals and metalloids. Other objectives of the study are to cluster the food waste materials through multivariate statistical computations of their FTIR spectra and to highlight possible correlations between the elements’ removal efficiency and the adsorbents’ chemical structures.Potato peel, lemon peel, orange peel, watermelon peel, tomato peel, coffee waste, apple peel, banana peel, decaf coffee waste, eggplant peel, carob peel and grape waste were sun-dried for a week, grinded with a mortar and then sieved to retain particles sized between 0.25 and 0.125 mm. The obtained powders were washed three times with deionized water produced by an integrate water purification system  and dried at 55 °C for 48 h in a vacutherm oven . Finally, the powders were weighed on an analytical balance  to obtain different amounts of each adsorbent: 25, 50, 100 and 200 mg.3  1% and NaOH  5%. Synthetic multi-element solutions at pH 2.0 and pH 5.5, containing Ag, As, Ba, Cd, Ce, Co, Cr, Cu, Fe, Ga, In, La, Mo, Ni, Pb, Sb, Sn, Th, Ti, U, V, W, and Zn at the concentration of 1 mg/Kg were prepared by mixing different aliquots of mono-element standard solutions  into 10 mL of aqueous solution. The pH of the solutions was controlled using a pH meter  and adjusted using HNO3 1% and NaOH 5%. After 24 h, the synthetic multi-element solutions were filtered using syringes with cryolite transparent membranes  to remove possible formed precipitates and then analyzed by inductively coupled plasma mass spectrometry  to identify the elemental fractions really dissolved in solution  before the adsorption processes.Aqueous solutions at pH 2.0 and pH 5.5 were prepared using deionized water, HNOWastewater produced in a hydro-metallurgical process for the recovery of valuable elements by electronic boards (WEEE—waste of electric and electronic equipment) was used to verify the efficiency of the 12 food waste adsorbents for the removal of heavy metals from a real polluted matrix. The wastewater was obtained after the acid leaching of electronic boards and the following removal of valuable elements through fractional precipitation of hydroxides. It is a very complex and variable matrix, and contains a high amount of salts as well as a high variable content of elements: Al < 100 µg/L; Ba = 318 µg/L; Cr = 146 µg/L; Cu = 377,000 µg/L; Fe < 200 µg/L; Mn = 7730 µg/L; Ni = 233,000 µg/L; Pb = 72,100 µg/L; Sn < 20 µg/L; and Zn = 2,650,000 µg/L . Before −1 with a resolution of 5.0 cm−1.The powders of the 12 food waste materials were analyzed by scanning electron microscopy  to assess their superficial aspect and porosity and by Fourier transform infrared spectroscopy  to identify the types and the amounts of functional groups present on their adsorbent surfaces. The powders were fixed to a specific support and coated with a thin layer of platinum metal before SEM analysis to make them surfaces conductive. The IR spectra were recorded in the range of 5000–600 cmAdsorption experiments were performed by treating each of the 12 food waste adsorbents with 10 mL of the synthetic multi-element solution  under controlled pH conditions (at pH 2.0 and pH 5.5). Increasing amounts  of each adsorbent were exposed, in different experiments, to the multi-element solutions (at pH 2.0 and pH 5.5). The pH of the solutions was controlled after the exposure of each adsorbent which may cause slight changes of pH. A final adsorption experiment was performed treating 200 mg of each of the 12 food waste adsorbents with 10 mL of the diluted wastewater (pH 5.5) produced in a hydro-metallurgical process. Before performing the adsorption experiments, solubility percentage of each element of the synthetic multi-element solution at different pH was verified .i) is very low and it is not possible to accurately quantify their removal. Most of the considered elements completely precipitate at pH higher than 5.5; therefore, the adsorption experiments were not performed at basic conditions.At pH 2.0, elements’ solubility percentages are very high (except for W), thus at these acidic conditions it is possible to quantify the adsorption of the elements obtaining good quality analytical data. At pH 5.5, As, Ce, Fe, Ga, La, Pb, Sn, Th, Tl and U form insoluble hydroxides; consequently, their initial concentration (CThe aqueous solutions containing different food waste adsorbents (blanks) and the synthetic multi-element solutions with and without the adsorbents were magnetically stirred  at 25 ± 2 °C and 200 rpm for 24 h to ensure the achievement of the equilibrium between the adsorbates and the adsorbents. After 24 h, the samples were filtered using a glass filtering system with nitrocellulose membranes , the pH of the solutions was controlled. All the adsorption experiments were performed in duplicate. Finally, samples were analyzed by inductively coupled plasma mass spectrometry . Blank values due to the elements’ release from the adsorbent materials were also evaluated. Details about the used instrumental conditions are reported in Canepari et al. (2006) and Protano et al. (2016) ,43. i—initial concentration). In this way, the precipitated fractions of the elements, not available for the adsorption processes, were not considered as removed by the food waste materials.The elements’ adsorption percentages were calculated from the elemental fractions dissolved in the synthetic multi-element solution after 24 h from its preparation (after precipitation phenomena) and after its filtration , the difference between the elemental fraction not removed by the food waste adsorbent (Ce—equilibrium concentration) and the elemental fraction released in solution by the adsorbent itself . This value was divided by the concentration of the element dissolved in the initial multi-element solution  and multiplied per 100:For each food waste adsorbent, the elements’ adsorption percentages were calculated by subtracting to the concentration of the elements dissolved in solution before the adsorption processes . A first principal component analysis (PCA) was calculated on the spectral data obtained by FTIR spectroscopy of the food waste adsorbents in order to group the 12 materials as a function of the type and amount of functional groups present on their surfaces. A standard normal variate (SNV) autoscaling was operated to the spectral data before performing the PCA to correct multiplicative variations between the spectra caused by variations in sample physical properties or in sample preparation and presentation. A second PCA was performed on the data obtained by the adsorption experiments to group the food waste materials according to the obtained elements’ adsorption percentages.Different micrographs , which sThe 12 food waste materials showed various superficial structures characterized by peculiar conformations and different abundance, morphology and size of pores. Various grades of porosity correspond to different superficial specific areas available for adsorption processes. Therefore, from this first qualitative observation, we can suppose that the food waste materials with the most porous surfaces may be able to remove a higher amount of elements from polluted solutions.−1 are shown in By Fourier transform infrared (FTIR) spectroscopy, functional groups present on the adsorbent surfaces were identified. The FTIR spectra of the 12 food waste materials in the range of 5000–600 cm−1 includes many vibrations modes corresponding to −OH of alcohols [−1 and 2855 cm−1 belong to the C−H bonds of methyl and methylene groups of lipids [−1 corresponds to the C=O vibration in the carbonyl group of −COOH. The bands in the range of 1600−1400 cm−1 can be ascribed to C=C vibration of lipids, fatty acids and lignin moieties [−1 to 1400 cm−1 are less intense in potato peel, carob peel and eggplant peel spectra. The region at 1400−900 cm−1 shows several types of vibrations including C−H, C−O−C, C−N and P−O of polysaccharides [The broad band in the range of 3700 and 3000 cmalcohols , phenolsalcohols . This ref lipids . These bmoieties ,45,46,47charides ,48. ThisBy the recorded FTIR spectra, similarities and differences of the chemical structures of the adsorbent surfaces were identified. The spectral data obtained by FTIR spectroscopy were elaborated by performing multivariate statistical computations. Principal component analysis (PCA) of the spectral data allowed to group the food waste materials according to functional groups present on their adsorbent surfaces. Food waste materials are efficient adsorbents for the removal of many metals and metalloids from synthetic multi-element solutions at pH 2.0 and pH 5.5. At pH 2.0, some elements, such as Mo, Pb, Sb, Sn, Th, Ti and W, were adsorbed in high percentages by most of the food waste materials while others were removed in different percentages depending on the adsorbent exposed. For example, As was removed only by banana peel (adsorption percentages ~37%) and by carob peel, which was able to adsorb more than 75% of the As dissolved in solution. At pH 5.5, the food waste materials resulted less selective for the adsorption of Ag, Cd, Mo, V and Zn, which were removed in high percentages by most of the adsorbents. Decaf coffee waste, coffee waste, potato peel, apple peel, eggplant peel and carob peel resulted more efficient at pH 5.5 than at pH 2.0 for the removal of Ba, Cd, Ni, V and Zn, whereas orange peel appeared more efficient at pH 2.0. W was removed in higher percentages at pH 2.0 by all the adsorbents except watermelon peel. Orange peel and tomato peel removed in higher percentages Ag and Cu at pH 2.0, while coffee waste, decaf coffee waste, apple peel, eggplant peel and carob peel appeared more efficient for the removal of these elements at pH 5.5. Finally, orange peel adsorbed more than 85% of Zn at pH 2.0, whereas apple peel, carob peel, coffee waste and decaf coffee waste were able to remove more than 85% of Zn only at pH 5.5.Banana peel, watermelon peel and grape waste resulted the most efficient adsorbents for the removal of most of the metals and metalloids from multi-element solutions at pH 2.0 as well as at pH 5.5. Elements’ removal efficiency varies depending on the amount of adsorbent exposed in solution, as expected from the adsorption isotherms defined in previous studies ,38,39,40In Standard deviations of the results obtained by the adsorption experiments (performed in duplicate) are all under 25%.Each food waste material showed a propensity to remove certain elements rather than others depending on the functional groups present on its adsorbent surface.Principal component analysis of the data obtained by the adsorption experiments allowed clustering the food waste materials according to their elements’ adsorption percentages. Comparing these results with the results obtained by the multivariate statistical analyses of the FTIR spectra allowed to highlight the potential correlations between the adsorbents’ efficiency and their specific chemical structures. The adsorbents in Therefore from the statistical elaboration of the obtained results we can suppose that the elements above the loading plot’s center of The removal efficiency of the food waste adsorbents was verified on a real polluted matrix assessing the adsorption of heavy metals from the heavy metal wastewater produced in a hydro-metallurgical process. These data confirm the results obtained by the adsorption experiments performed in synthetic multi-element solutions. However, food waste materials resulted less selective for the removal of metals and metalloids from the wastewater because of the lower competitiveness of the four heavy metals for the more available active sites of the adsorbent surfaces. On the other hand, banana peel, watermelon peel and grape waste resulted the most efficient and the least selective food waste adsorbents for the elements’ removal from the heavy metal wastewater as well as from the synthetic multi-element solutions. Although the use of multi-element solutions does not allow the individuation of the optimum elements’ removal values, it enables evaluating and comparing the efficiency of the individual food waste adsorbents for the removal of various elements from a matrix close to a real one.Many food waste materials used as low cost adsorbent for the treatment of wastewater were reported in the past. This work was aimed to evaluate the adsorption capacities of most of the studied food waste materials comparing their efficiency for the removal of more than 20 elements from complex multi-element solutions, in homogeneous experimental conditions. The pH selected for adsorption experiments were pH 2.0 and pH 5.5. The latter pH value is just before the pH-zone of 5.5–8.0, where precipitation phenomena dominates. Elements’ removal efficiency was evaluated from complex multi-element solutions to assess the potential of the food waste materials in industrial applications.Maintaining homogeneous experimental conditions offered the possibility to evaluate and compare the adsorption capacities of 12 food waste materials.Banana peel, watermelon peel and grape waste resulted the most efficient and the least selective adsorbents for the removal of most of the metals and metalloids from multi-element solutions (at pH 2.0 and pH 5.5) as well as from heavy metal wastewater.The adsorbent surfaces were analyzed by FTIR spectroscopy and showed different types and amounts of functional groups, which demonstrated to act as adsorption active sites for various elements. The 12 food waste materials were grouped, through multivariate statistical computations of the FTIR spectra, into five different clusters, depending on the functional groups present on their adsorbent surfaces.By comparing these results with those obtained by the multivariate statistical analyses of the elements’ removal percentages, it was possible to highlight the potential correlations between the adsorbents’ efficiency and their specific chemical structures.


Forty percent of American women are obese and at risk for type II diabetes, impaired immune function, and altered microbiome diversity, thus impacting overall health. We investigated whether obesity induced by an excess calorie, high fat diet containing hydrogenated fats, fructose, and coconut oil (HFD) altered glucose homeostasis, peripheral immunity, and urogenital microbial dynamics. We hypothesized that HFD would cause hyperglycemia, increase peripheral inflammation, and alter urogenital microbiota to favor bacterial taxonomy associated with inflammation. We utilized female Ossabaw mini-pigs to model a ‘thrifty’ metabolic phenotype associated with increased white adipose tissue mass. Pigs were fed HFD  or lean  diet for a total of 9 estrous cycles (~6 months). To determine the effect of cycle stage on cytokines and the microbiome, animals had samples collected during cycles 7 and 9 on certain days of the cycle: D1, 4, 8, 12, 16, 18. Vaginal swabs or cervical flushes assessed urogenital microbiota. Systemic fatty acids, insulin, glucose, and cytokines were analyzed. Pig weights and morphometric measurements were taken weekly. Obese pigs had increased body weight, length, heart and belly girth but similar glucose concentrations. Obese pigs had decreased cytokine levels , arachidonic acid and plasma insulin, but increased levels of vaccenic acid. Obese pigs had greater urogenital bacterial diversity, including several taxa known for anti-inflammatory properties. Overall, induction of obesity did not induce inflammation but shifted the microbial communities within the urogenital tract to an anti-inflammatory phenotype. We postulate that the coconut oil in the HFD oil may have supported normal glucose homeostasis and modulated the immune response, possibly through regulation of microbial community dynamics and fatty acid metabolism. This animal model holds promise for the study of how different types of obesity and high fat diets may affect metabolism, immune phenotype, and microbial dynamics. Obesity is at epidemic levels, particularly in western cultures such as the United States, where recent epidemiologic statistics indicate that 40% of American women are obese . ObesityEarly diagnosis of immunological and microbial community dysregulation could aid in early detection of obesity-associated inflammation and thus diminished overall health. Several studies have demonstrated the role of the microbiome in the development of the host immune system, as mice lacking microbes in the gastrointestinal tract fail to undergo immune cell maturation and have a dysregulated immune response , 8. Disr3 isoform of AMP-activated protein kinase. Upon activation, this kinase phosphorylates enzymes involved in insulin signaling and cholesterol and fatty acid metabolism, and is therefore associated with accumulation of increased subcutaneous, visceral, and intramuscular fat and a ‘thrifty’ phenotype [Previous studies have utilized the Ossabaw pig as an ideal model for the study of obesity and its effects on reproductive function in females. Ossabaw pigs have a loss of function mutation in the Val199→ Ile region of the PRKAG3 gene, which is the γhenotype .The goals for this study utilizing female Ossabaw pigs as a model of obesity were to: 1) measure body weight and measurements, glucose and insulin concentrations, and plasma free fatty acid composition to assess metabolic function, 2) assess systemic immune function through analysis of serum cytokine levels, and 3) identify bacterial communities in the urogenital tract associated with obesity. Specifically, we hypothesized that obesity induced by a high fat diet rich in hydrogenated fats and a plant-derived saturated fat, coconut oil, would cause hyperglycemia, hyperinsulinemia, decrease the immune protection of the urogenital microbial community profile, and favor systemic inflammation.Guide for the Care and Use of Laboratory Animals [ad libitum. The obese phenotype was induced over three months of dietary treatment (induction phase), following which the diets continued to be fed at the same levels for an additional 3 months (maintenance phase). During the study, pigs were housed individually to ensure exact dietary dosing and were on a 12:12-hour light:dark cycle. Euthanasia was performed by sodium pentobarbital overdose following administration of 1 ml/45 kg body weight of a combination of telazol + ketamine + xylazine. Every effort was made to minimize animal suffering.All experimental procedures performed on animals were done by a protocol approved specifically by the University of Illinois at Urbana-Champaign Institutional Animal Care and Use Committee , and followed the guidelines as described in the  Animals . Five nuThe design of this study used repetitive sampling from each individual, which allowed for each animal to serve as their own internal baseline control, and thus generate substantial, significant data with a limited sample size. Heat detection was conducted in the presence of a boar and was used to measure estrous cycle length to determine sampling time periods. Jugular blood collection, vaginal swabs, and cervical flushes were performed on day 1  and on day 8  of the estrous cycle for the first six continuous estrous cycles (induction phase). Samples were also collected on cycles 7 and 9 (maintenance phase), on days 1 (estrus), 4, 8, 12, 16, 18 or until pigs stood in heat again. A Panepinto low stress sling restraint system was used on all pigs for the purposes of restraint for blood collection [Physical measurements were taken weekly, including weight, crown to rump length, heart girth, belly girth, and height. Blood glucose concentrations were measured on D1, 4, 8, 12, 16, 18 for cycles 7 and 9 from blood obtained by small ear pricks. Glucose testing was performed with the Precision Xtra blood glucose and ketone monitoring system , which has been validated for use in the Ossabaw pig . Free fa®, Life Technologies Ltd., UK). Analysis of the assay was performed on a Luminex®100/200™ in the Research Services Core at Southern Illinois University School of Medicine. Cytokine levels were measured and compared within each individual and also across all pigs to minimize variability. Samples were taken at multiple time points during cycles 7 and 9 (maintenance) to gauge whether reproductive cyclicity had an effect on the levels of each cytokine.The levels of seven cytokines (Interleukin 1-beta (IL-1β), Interleukin 10 (IL-10), Interferon alpha (IFN-α), Interferon gamma (IFN-γ), Tumor necrosis Factor alpha (TNF-α), Interleukin 4 (IL-4), and Interleukin 8 (IL-8)) in serum from days 1, 8, 12, 16, and 18 of cycles 1, 7 and 9 of each pig were analyzed simultaneously using the Swine Cytokine Magnetic 7-Plex Panel assay  according to the manufacturer’s instructions. After extraction the DNA stock concentration was measured using a NanoDrop-2000 spectrophotometer , with samples ranging from 12.6 to 510 ng/μl.DNA was extracted from the vaginal swabs and cervical flushings of all pigs using the PowerSoil5’TATGGTAATTGTCCTACGGGAGGCAGCAG3’; read 2: 5’AGTCAGTCAGCCCCGTCAATTCMTTTRAGT3’; index: 5’ACTYAAAKGAATTGACGGGGCTGACTGACT3’). The universal reverse primer was 926R (5’CCGTCAATTCMTTTRAGT3’). One of 96 specific barcodes consisting of 12 base pairs was also added to the reverse primer. These unique barcodes serve as a genetic ID for each sample to be sequenced. The PCR cycle was as follows: 45 seconds at 98°C followed by 25 cycles of 15 seconds at 98°C, 30 seconds at 65°C, and 30 seconds at 72°C for denaturation, and then 2 minutes at 72°C and hold at 4°C for final extension, as described in the HMP [A 584 bp fragment of the hypervariable V3-V5 region of the 16S rRNA gene was amplified by a polymerase chain reaction (PCR) as follows: 25 μl Kapa HiFi , 25 μM forward primer, 25 μM reverse primer, 50 ng of DNA, and molecular grade water to reach a final volume of 50 μl per reaction. The 357F forward primer was used  and cycle 7 day 1 were used to calculate the change in free fatty acids within each animal over time. Insulin concentrations on cycle 1 days 1 and 8 and cycles 6, 7, and 9 days 1 and 8 were used to calculate the change in insulin within each animal over time while on their respective diet. This same comparison was made for the HOMA-IR parameter across treatment groups. This type of calculation was not performed on glucose concentrations, because glucose concentration initially was affected by the stress of handling, which resulted in glucose levels being high in both treatment groups at the onset of the study.All metabolic data were assessed using PROC GLM  for normality and transformed as needed for non-normal samples prior to analysis. A repeated measures in time ANOVA was conducted . The Akaike criterion was used to determine the best fit covariance matrix, which was the autoregressive matrix, and all parameters were run using this matrix.Cytokine concentrations at each time point within each animal were normalized to the animal’s own estrous cycle 1, day 1 (induction), meaning that statistical differences for each individual were calculated as the change between sampling timepoints , and then this difference was compared between all individuals. All samples were normalized to the background fluorescent intensity (no serum control) from the unknown sample fluorescent intensity, which was calculated from the standard curves provided in the assay kit, which yielded average concentrations across treatment groups. A Wilcoxin signed rank test was conducted to determine significance between treatment groups throughout the treatment timeline.To determine the relationship between microbiomes, data were visualized with 2D or 3D non-metric multidimensional scaling (NMDS) plots, which were constructed by assigning a non-parametric monotonic relationship to the Bray-Curtis dissimilarities using isotonic regression to produce a two- or three-axis representation of the relationships between data. Subsequently, a permutational MANOVA test was used to assess the factors driving bacterial community composition. Bray-Curtis distance analysis, coordinate, and statistical analyses used to cluster microbial profiles were determined as previously described .p<0.05 was the criterion to indicate statistical significance unless otherwise noted.All results are presented as the least squared mean ± standard error of the mean. In all statistical tests, P ≤ 0.05) greater in obese pigs compared with lean (P = 0.04) while on the diet as compared with lean pigs (P = 0.10) while on the obese diet as compared to lean pigs , crown to rump length , heart girth , and belly girth  were all significantly  plots . The axeLactobacillus were then analyzed between treatment groups during both the induction phase and maintenance phase , Firmicutes , and Proteobacteria  . The levce phase . Copy nuLachnospiracea and Incertae are the genera with the highest increase in copy number in obese pigs, with a 22.47 and 18.92 fold increase compared to lean pigs, respectively. Both of these genera are associated with protective or anti-inflammatory properties [Lactococcus, Paraprevotella, Streptococcus, and Pseudomonas bacterial genera all have a reduced abundance in obese pigs . Of these, species within the genera Paraprevotella, Streptococcus, and Pseudomonas are associated with either inflammatory or disease states [After sequences were clustered into OTUs, bacterial taxa that either had increased abundance or decreased abundance were identified in obese animals when normalized to lean animals . Lachnosoperties , 24. Lace states –27.The goal of this study was to investigate the relationship between obesity induced by a high fat diet rich in hydrogenated fats and a plant-derived saturated fat, coconut oil, on metabolism and immune and microbial parameters using the Ossabaw mini-pig as a model for obesity. Rodents are a common animal model used to study obesity; however these animals may not mimic the pathogenesis of obesity observed in humans . FurtherAlthough coconut oil has been shown to improve glucose homeostasis , 33 and The free fatty acid profile identified increased levels of vaccenic acid and decreased levels of arachidonic acid in obese pigs compared to lean pigs. Vaccenic acid is a trans fatty acid, and may have a beneficial role in reducing risk factors of cardiovascular disease –40. In cLachnospiracea and Incertae have the highest increase in copy number in obese pigs and are associated with protective or anti-inflammatory properties. Lachnospiracea species are decreased in the gut microbiome in patients with inflammatory bowel disease [C. difficile [Incertae species within the gut are associated with reduced intestinal inflammation in patients with Crohn’s disease [Incertae species within the gut in patients with cirrhosis, and also that a less robust immune response is initiated against these species, concluding that the presence of these bacteria are associated with decreased inflammation [Paraprevotella, Streptococcus and Pseudomonas, which have reduced abundance in the urogenital tract of obese pigs when compared to lean pigs, contain species that are linked to inflammatory and/or disease states. Paraprevotella is closely related to Prevotella species, which are associated with the pathogenesis of rheumatoid arthritis [Streptococcus pneumoniae infection, the causative agent for bacterial meningitis, is well known [Pseudomonas contains the species Pseudomonas aeruginosa, the established infectious agent in cystic fibrosis patients that results in a strong inflammatory response [Lactococcus species within the urogenital tract, which are lactic-acid producing commensal bacteria. Lactic acid serves to protect the vaginal canal by several mechanisms, including lowering the pH of the vaginal canal, ultimately creating an environment that disallows pathogenic bacteria from flourishing and causing infection [Lactococcus in the female urogenital tract is not as well characterized as Lactobacillus species, warranting further investigation on the extent of the protective effects Lactococcus offers.Identification of bacterial taxa that have either increased or decreased abundance in obese compared to lean pigs in the urogenital tract revealed that obese pigs were more protected from inflammation within the urogenital tract. These data are novel as the influence of obesity on urogenital microbiome community dynamics has never been reported but the influence of obesity on gut microbial function and inflammation has been well established . Therefo disease , and havifficile . Incerta disease . Furtherammation . Conversrthritis . The infll known . The genresponse . Interesnfection . HoweverObese pigs had reduced serum levels of both pro-inflammatory and anti-inflammatory cytokines compared to lean animals. Increased TNF-α concentrations from adipose tissue are typically observed in obese individuals, and are associated with insulin resistance , 51. HowThe microbial composition of the gut is associated with obesity perhaps by influencing metabolic function, gut permeability, and inflammation –53. A sein vivo [Listeria monocytogenes [The fact that obese pigs fed a high fat diet rich in coconut oil have decreased inflammation and levels of arachidonic acid is intriguing, since obesity alone is known to drive inflammation. One key difference between the high fat in this study, the high fat diet used in previous Ossabaw studies by Newell-Fugate et. al., and the lean diet was the inclusion of coconut oil in the high fat diet for this current study , 14. Cocin vivo , 59. Addin vivo , 61. It in vivo . More inytogenes . Based oIn conclusion, when fed an excess-calorie, high-fat/cholesterol/fructose diet rich in coconut oil, female Ossabaw pigs developed obesity but were normoglycemic, demonstrated decreased markers of inflammation, and had greater bacterial diversity in the urogenital tracts than lean pigs. Obesity has been implicated as a major cause of type II diabetes and infertility. However, our results indicate that obesity alone may not be solely responsible for these pathologies. Instead, dietary fat source may modulate glucose homeostasis in the context of obesity, possibly secondarily to the inflammatory environment. Our results suggest that dietary modifications may be beneficial to modulate obesity to a “healthy phenotype”. Because the development of obesity is multifactorial, the Ossabaw pig is a useful animal model in which to study the underlying mechanisms of this condition and the influence of diet within the context of obesity.


Perca fluviatilis) in the diet of European catfish (Silurus glanis) caught by longlines in Milada and Most Lakes, Czech Republic. The finding proves that perch egg strands compose a standard food source for this large freshwater predatory fish. It extends the present knowledge on catfish foraging plasticity, showing it as an even more opportunistic feeder. Utilization of perch egg strands broadens the catfish diet niche width and represents an advantage against other fish predators. Comparison of datasets from extensive gillnet and SCUBA diver sampling campaigns gave the evidence that at least in localities where food sources are limited, multilevel predation by catfish may have an important impact on the perch population.So far, perch egg strands have been considered unpalatable biological material. However, we repeatedly found egg strands of European perch ( Perca spp.) egg strands as repellent matter for predators. Observation and testing of six fish and four invertebrate species proved that perch egg strands are an undesirable food source for them. One year later, Diamond and Wakefield [A study published 31 years ago by Newsome and Tompkins [akefield  publisheakefield .Silurus glanis), one of the world biggest freshwater fish, is a typical opportunist with a wide diet niche. It has successfully spread worldwide accompanied by human activity [European catfish  egg strands are a part of the European catfish diet. Further, we discuss the potential impact of European catfish on perch populations as a result of multilevel predation.Although the European catfish is a top predator, only a few studies regarding its diet have been carried out, and most of them are recent (for review see ). The ma6 m3 and maximum depth of 25 m (Esox lucius) was introduced in 2005  and European catfish in 2007 , both for biomanipulation purposes. The oligotrophic Most Lake has an area of 310 ha, volume of 70×106 m3 and maximum depth of 75 m  and a fishing treble. The predator (catfish or pike) was hooked by the fishing treble hanging under the baited hook while it tried to catch and tear down the bait. Altogether 30 individual bait fish on 3 longlines were used each day of sampling. To cover the shore area of the lake evenly, the lines were moved each day to a new place and they were checked three times per day . Most of the catfish were caught during the night, most of the pike during the day. All individuals were measured, weighed and non-invasive stomach content analyses were provided. In the case of catfish, stomach content was extracted by hand through the opened mouth and gullet [European catfish and northern pike from both lakes were caught by longlines in early May 2014 and 2015 during a 4-day-and-night-long campaign see T = 0–300 mm d gullet . The fisd gullet .To obtain quantitative assessment of perch egg strands, SCUBA divers monitored their occurrence, numbers and sizes at transects from the shore to a depth of ~20 m in April and May in 2015 .2. Only 6% and 2% of fish community was annually caught by gillnets in Milada and Most Lakes, respectively. In case of perch population, it was 9% and 2% in Milada and Most Lakes, respectively .Sizes of female perch that had contributed to the spawning were estimated from the width of individual egg strands, using the equation of Dubois et al. . Perch aWater temperature measurements were taken between 12:00 and 14:00 during the sampling campaign in both years (2014 and 2015) and lakes. Measurements were made at 1 m depth intervals using a calibrated YSI 556 MPS probe . Temperature of epilimnion was used for purpose of this study.T were used in the analysis. This size limit was set for two main reasons: 1) The SCUBA diving results from 2007 and 2009 have shown that in Milada Lake 97% and 92% of perch egg strands were spawned by females ≥ 230 mm LT [T were females [T caught by 1,000 m2 of gillnets in September of year X-1 and the CPUE of egg strands found by SCUBA divers (egg strands per 10 hours of diving) during spawning season in following spring, i.e. April/May of year X. The CPUE of perch caught by gillnets was calculated as the mean of catches of perch individuals ≥230 mm LT over all depth layers of benthic and pelagic habitats. This comparison is not biased by the growing season because female perch of 230 mm LT caught in September of year X-1 will be of approximately the same size in April/May of year X . The stails see . A nonpaT range 710–1580 mm, weight range 0.8–23.5 kg). Altogether, 56 food items in the stomachs of 37 individuals were found. Perch egg strands were recorded in ten different catfish .Perch egg strands were not detected in stomachs of catfish in Milada Lake, in 2014, but in 2015. Excluding Milada Lake 2014, perch egg strands composed 13–71% of identified diet items  were caught in Milada and Most Lakes during spring sampling in 2014 and 2015 . In Milada Lake 2015, 23 perch egg strands were found during six dives in three consecutive weeks . The width of 11 of them was 30–40 mm, corresponding with LT of perch female 180–213 mm, and 12 of them were wider than 40 mm, corresponding with LT of perch female >230 mm.In Most Lake 2015, SCUBA divers found 3 perch egg strands of width >40 mm, corresponding with LT ≥ 230 mm) in Milada Lake was observed only between the years 2006 and 2007. A decrease in perch abundance has been observed since 2007, the year when catfish were stocked. A similar trend has been observed by SCUBA divers during perch egg strand monitoring. A strong relationship was found between the CPUE of perch ≥230 mm LT caught by gillnets and the CPUE of perch egg strands found by SCUBA divers during the following springs  along with a perch egg strand, but the strand width of 55 mm corresponds with perch female of 256 mm LT [It is clear that perch egg strands were eaten after they were spawned because the occurrence of jelly coat indicates their deposition in water . No fish56 mm LT  implying56 mm LT , 22,24. i.e. 12°C [T per 1,000 m2 of gillnets; [-1, 7.65 kg ha-1, mean LT 850 mm) than in Milada Lake . The catfish population represents 24.4% and 39.3% of the total fish biomass in Milada and Most Lakes, respectively. In Milada Lake, the populations of the top predator (only) is close to the average biomass of all predators (including e.g. perch ≥15 cm standard length) calculated for other aquatic ecosystems of a similar size in the Czech Republic [Perch egg strands were present in catfish stomachs in the case of Most Lake 2014 and 2015 and also Milada Lake 2015. Catfish most likely utilized perch egg strands also in Milada Lake in 2014, but water temperature of 16.3°C during longline sampling was already much higher than the optimal temperature for perch spawning, .e. 12°C , 29, 24 illnets; ,31). NevRepublic , in Mosti.e. 8.6 pcs. per 10 hours of diving) was probably influenced by high predation pressure by the smaller catfish. It is supported by the fact that catfish with perch egg strands in the stomachs were significantly smaller (mean 867 mm LT) than catfish with other food items in the stomachs (1067 mm LT). Smaller individuals are gape limited and their prey capture efficiency is lower, therefore readily available food sources such as egg strands are preferred. In Most Lake in 2015, catfish presence in perch spawning areas was also confirmed by SCUBA divers, where nine catfish were observed (per 3.5 h of diving) giving the ratio of three catfish to one perch egg strand.The low number of perch egg strands found in Most Lake in 2015  exhibit a reproductive style unique among teleosts (single ovary is enclosed in a membrane forming an ovisac), the yearly effort of a perch female committed to reproduction is comprised of one item (egg strand) with only one defence–unpalatability [Since perch Click here for additional data file.


Cynoscion othonopterus) with simultaneous measurements of density from active acoustic surveys in the Colorado River Delta, Mexico. During the formation of FSAs, we estimated peak abundance at 1.53 to 1.55 million fish, which equated to a biomass of 2,133 to 2,145 metric tons. Sound levels ranged from 0.02 to 12,738 Pa2, with larger measurements observed on outgoing tides. The relationship between sound levels and densities was variable across the duration of surveys but stabilized during the peak spawning period after high tide to produce a linear relationship. Our results support the use of active acoustic methods to estimate density, abundance, and biomass of fish at FSAs; using appropriately scaled empirical relationships, sound levels can be used to infer these estimates.Sound produced by fish spawning aggregations (FSAs) permits the use of passive acoustic methods to identify the timing and location of spawning. However, difficulties in relating sound levels to abundance have impeded the use of passive acoustics to conduct quantitative assessments of biomass. Here we show that models of measured fish sound production versus independently measured fish density can be generated to estimate abundance and biomass from sound levels at FSAs. We compared sound levels produced by spawning Gulf Corvina ( Some of these challenges are minimized if surveys are conducted where and when fish form large conspecific spawning aggregations4. Fish spawning aggregations (FSAs) occur at predictable times and locations, permitting surveys to estimate spawning stock abundance, biomass, and length and age distributions of regional populations6.Quantitative assessments of fish abundance form the basis of fisheries management and conservation strategies7. However, these data can be insensitive to population declines since fish densities at FSAs may not change proportionally with abundance9, making these methods inefficient for monitoring, assessments, and establishing quotas of total allowable catch. Fisheries-independent data often yield more accurate assessments of stocks10, but methods such as mark-recapture, visual census, and trawls can be invasive, laborious, expensive, and inefficient or ineffective across the diverse environments where FSAs occur12. Therefore, the continued development and expansion of fisheries-independent methods to survey FSAs in challenging habitats are greatly needed to improve the quality of population assessments and manage vulnerable spawning stocks in a sustainable manner6.Efforts to monitor FSAs and manage fishing intensity often rely on fisheries-dependent data  to estimate stock abundance13 and shallow water habitats15 enables assessments of populations across a spectrum of environments where constraints of depth and low visibility prevent the use of other methods. As a trusted source of fisheries-independent data for resource managers and policy makers17, active acoustic surveys have been used to estimate abundance, biomass, and length and spatial distributions of fish stocks at FSAs19. However, the cost and complexity of collecting and processing data have impeded the widespread application and long-term use of active acoustics for assessments of most aggregate spawning fishes, which are often associated with small-scale fisheries in developing countries6.The adaptability of active acoustic methods to survey fishes in both deep21 has prompted an insurgence of passive acoustic methods to non-invasively and efficiently study and monitor populations24. As many fishes produce species-specific calls associated with courtship and reproduction, recordings of sound production have been used to identify the timing and location of spawning in a number of exploited species, such as Atlantic Cod18 (Gadus morhua), Pollock25 (Pollachius pollachius), Haddock26 (Melanogrammus aeglefinus), Nassau Grouper27 (Epinephelus striatus), and numerous species of croakers29 (family Sciaenidae). While some progress has been made to use received sound levels as indices of fish presence, behaviors, and relative abundance at FSAs34, difficulties in relating sound production metrics to fish densities have precluded passive acoustic estimations of abundances for use in quantitative population assessments35.Increased recognition of sound production in over 100 families of marine fishes36; however, call rates at FSAs often vary considerably38 and are difficult to characterize in-situ for fishes. Additionally, fish choruses at some FSAs prevent the isolation of individual calls29. A more tractable approach may be to utilize relationships between sound production levels, measured using passive acoustics, with independent measures of fish density estimated with active acoustics35. Ultimately, species-specific models could be used to assess and monitor spawning stock densities, abundances, and biomasses using passive acoustic methods during periods with stable rates of sound production40.With an understanding of fish calling rates and sound propagation, it is possible to estimate abundance from call occurrences42 within FSAs that form in estuaries and coastal habitats44 , hereafter Corvina, at its FSA and constructed a model to quantify relationships between sound levels and fish density during spawning. From late February to early June, during the outgoing tide over 2–3 day periods prior to new and full moons49, Corvina migrate from as far south as the Midriff Islands  across surveys (Table L) 21–100 cm, as estimated from the following Kirchoff-ray Mode (KRM) model-derived relationship between mean lateral-aspect 120-kHz target strength (TS) and Corvina L (cm):Mean densities (±S.E.) of Corvina ranged from 0.43 ± 0.07 to 15.97 ± 2.04 fish/1000 mL (±95% C.I.) of the aggregated fish ranged from 38.677 ± 2.761 cm to 53.402 ± 1.407 cm among surveys, corresponding to mean weights (W) of 0.578–1.464 kg  over the frequency band of Corvina chorusing , coinciding with spawning; mean p2 (±95% C.I.) ranged from 1.56 ± 1.9 Pa2 on the incoming tide of 28 April to 4430 ± 1004.0 Pa2 on the outgoing tide of 27 April 2014. Spatial differences in p2 showed similarities to measured differences in fish density across the spawning grounds during the outgoing tides but not during the incoming tides  attributable to dense aggregations and spawning activity over distances of 6.5 to 25 km on the outgoing tides of 27 and 28 March, and 27 and 28 April 2014.We recorded Corvina sound production associated with spawning during all survey days. We did not observe sound production by any other fish species in our recordings. Measurements of mean square pressure amplitude  resulted in different relationships dependent on the timing of measurements in relation to high tide  and significantly different from all other hours , indicative of stationary call rates of Corvina that are additive in p2 as a function of density during the two-hour window  with the following equation:Comparisons between dow Fig. . The modide Fig.  resulted1, Fig. ρ, a fundamental measurement for population assessments, across different spatial scales  with near-zero visibility and large tidal ranges (7 m over a 6 hour period), which inhibits visual surveys; spawning takes place within a no-take biosphere reserve thereby discouraging invasive trawl sampling; and heavy fishing mortality during stock migrations and some spawning events within the protected reserve conflicts with mark-recapture, telemetry, and extractive methodologies. In this study, the versatility of active acoustic methods overcame these challenges to elucidate spawning and stock dynamics of Corvina and produce measurements of fish density, abundance, and biomass in a non-invasive manner.The selection and validity of different methods to estimate density for comparison to passive acoustic measurements are often challenged and limited by resource and equipment availability, environmental conditions, and spatial and reproductive dynamics of target species52. These challenges were mitigated by orienting the acoustic beam axis nominally 10° below horizontal to detect Corvina up to 50 m away from the vessel and removing the first 10 m range of acoustic data that exhibited low detection probability, respectively. As opposed to echo-integration16, fish were detected as tracks with multiple, spatially coherent TS measurements, permitting the calculation of density, abundance, and TS distributions of aggregated Corvina. The selection of Corvina-specific detection parameters and a lack of bycatch in the fishery50 provide credence that our estimates were not influenced by the presence of additional species. KRM modeling provided an equation to relate mean lateral-aspect TS to fish length for Corvina, thus enabling the estimation of mean fish lengths, weights, and total biomass.Our active acoustic surveys generated the first fisheries-independent estimate of Corvina density, abundance, mean length, biomass, and spatial distribution of mature fish across four days of spawning in 2014 that may supplement existing catch per unit effort data to develop more robust and comprehensive stock assessments for the fishery. Although the active acoustic survey results elucidated the dynamics of the aggregation and the fishery, they were difficult to conduct due to sound propagation in shallow water environments, low insonified volumes, and potential vessel avoidance by fish43. As we only focused our efforts in the northeastern channel due to logistical limitations, only a portion of the spawning grounds were sampled. Additionally, FSAs of Corvina form prior to the new and full moons in the months of February to June, but it remains unknown whether the entire reproductive population participates in all spawning events with fixed residency times, if individuals spawn only once or multiple times, if mean length and weight across all spawning events are constant, and what are the effects of fishing mortality during migrations to the delta. However, with the future documentation of these dynamics, active and passive acoustic estimates can be calibrated using a hierarchical model to determine the total abundance and biomass of the spawning population53.While this study observed the formation of multiple FSAs comprised of over 1 million fish, these data do not implicitly provide an estimate of the total spawning population. To assess the total abundance and biomass of spawning populations using active or passive acoustics, the spatio-temporal dimensions and reproductive dynamics of FSAs need to be known. For example, previous efforts that have tracked fishing intensity at the FSA have shown that the Corvina likely aggregate in both channels of the Colorado River Delta38, resulting in temporally inconsistent relationships between sound levels and abundance, as observed in this study. Consequently, the use of call numbers as predictors of fish abundance36 has proven challenging for researchers23. For many species, sound production associated with courtship and spawning is only present in males41, thus without a knowledge of sex ratios, the use of call rates and occurrences to estimate abundance and density may only be applicable to males, assuming factors affecting sound production rates are understood. However, it has been argued that the call rates of courting males may be influenced by the presence of females, where call levels may be indicative of both male and female density33 but these relationships have not been fully investigated. At some FSAs high abundances of fish and sound production prevent the detection of individual calls altogether, as in this study, requiring measurements of ambient sound pressure levels54 and the designation of indices to infer aggregation sizes32.Fish sound production rates at FSAs are difficult to measure and highly variable, often influenced by the timing of spawning activity35. Previous works have exemplified the potential of this method through comparisons of sound production indices with CPUE of simultaneous trawls39, densities of early stage eggs31, and asynchronous fish densities estimated with active acoustics40 and visual census33. In this study, we compared p2 over the bandwidth of Corvina chorusing to fish density estimated simultaneously with active acoustics. We selected p2 over other measurements, such as root-mean-square sound pressure level (dB), because the summed power from incoherent sounds from multiple calling fish is proportional to p2. By comparing the relationship of p2 with density over several one-hour periods, we were able to model the time-evolving relationship and identify patterns of similarity. The insensitivity of sound levels as a function of density prior to high tide and beyond two hours after high tide suggested inconsistencies in fish chorusing rates and a lack of coordinated courtship behaviors outside the peak spawning period. From high tide to two hours after high tide we observed a stable positive relationship, where increases in density corresponded linearly to increases in p2, indicating a two-hour period of homogeneous sound production rates during which density can be estimated from passive acoustic measurements. Importantly, comparisons of regression slopes provided an approach to identify periods of stationary sound production, thereby negating the need to estimate calling and chorusing rates of males and sex ratios in a challenging environment. However, this strategy assumed that sex ratios remained stable across measurements, which has been estimated at 1:1 from fisheries landings49.Given these challenges and uncertainties, we compared fish sound production to independent measurements of density, which may be a more feasible manner to model their relationship at FSAs55.As with most assessment methods, the application of the model developed in this study for population monitoring and fishery management is limited by the inherent uncertainty of density estimates from sound levels, warranting confidence intervals and the potential for future improvement with additional data and possible sources of variance, such as tidal current strength. However, this model presents an opportunity to assess Corvina density and calculate abundance with confidence intervals, during the two hours after high tide, using passive acoustic survey techniques. With this relationship, sound production measurements can be used to monitor the spatio-temporal distribution of density and abundance and quickly detect potential impacts of fishing activities on stock size and spawning activity. Estimates of abundance can also be used to calculate biomass if fish lengths at the FSA site are known through independent sampling. Lengths may also be estimated using passive acoustics through analyses of the fundamental frequencies of sounds35. Although our methods were applied to Corvina, they may be further developed and adapted to other commercially important species of soniferous fishes that form FSAs  at the University of California San Diego under IACUC protocol S13240 and at the University of Texas at Austin under IACUC protocol AUP-2015-00162.All methods were conducted in accordance with approved guidelines and regulations. The use of deceased fish from the fishery for 43, we focused our efforts on these days to assess the aggregation and minimize daily variation. We surveyed the spawning grounds of Corvina twice daily, on the incoming and outgoing tide, progressing in the direction of tidal current  fishing vessels. As there are only two days of peak spawning per FSA event (2–3 days before new and full moons)57 and a 38.1 mm diameter sphere constructed of tungsten carbide with 6% cobalt binder material . For each survey, we collected water temperature and salinity profiles using a handheld CTD  to measure sound speeds, calculate absorption coefficients, and calibrate data during processing. Throughout the surveys, the echosounder operated with a pulse duration of 256 µs, a ping rate of 0.25 s per transmission, and a transmit power of 200 W. The transducer was positioned off the port side of the vessel with the face nominally 0.53 m below the sea surface. As the survey area ranged in depth from 1 to 20 m, the beam axis was oriented nominally 10° below horizontal to increase the insonified volume, limit reverberation from the water surface, and increase the observational range. Surveys were performed at an average speed of 6 knots, and received power and split-beam phase data, indexed by time and geographic location, were sampled and stored every 64 µs.We conducted active acoustic sampling with a 120-kHz echosounder  configured with a split-beam transducer  having a nominal 9° beam width. Prior to sampling, we calibrated the complete system using the standard sphere methodWe recorded ambient sound at a single random position along each active acoustic transect  to parameterize the Kirchoff-ray Mode model58 (KRM) and estimate their target strength (TS) values at 120 kHz. The range of L approximated the size range  of mature Corvina at the FSA50. Each specimen was photographed, weighed, measured, and stored on ice before radiography. We radiographed fish ventrally and laterally at a distance of 116 mm with 48 kV at 15 mAs using a MinXray HF100+ mobile X-ray unit . Fish bodies and swim bladders were traced from X-ray images and used to parameterize the KRM. We estimated the average lateral-aspect TS for each length by summing the backscattering cross sections across incident angles of 65° to 115° , dividing by 180°, then converting to decibels58. The relationship between TS at 120 kHz and Corvina L (cm) was determined by a nonlinear least squares fit of the following equation:To identify Corvina in the active acoustic dataset and estimate mean TS and angular-position operands. A conservative, minimum-TS threshold of −46.5 dB was chosen, based on the mean KRM-TS versus L . We identified seabed echoes using an automatic detection algorithm and manual editing, and excluded them from further analyses. Echoes within the near-field range (0.51 m), corresponding to a beam-axis depth of 0.62 m, were also removed before further processing. We visually examined echograms to identify and remove regions of noise, propeller wash, bubbles, and along-shore segments of the surveys, resulting in a series of noise reduced across-channel transects for each survey. We identified individual targets attributable to Corvina using a single target detection operator  and 59 (m3) per bin to estimate the probability density function (PDF) of fish density versus distance from the transducer face and depth. Regions of data corresponding to ranges less than 10 m (2.3 m depth) from the transducer were excluded from further analyses, as a result of exhibiting a non-stationary density PDF attributed to low beam volume, fish avoidance of the vessel, or both52; beyond 10 m range (2.3 m depth) fish densities were stationary and homogenous. We separately partitioned the remaining data (range 10 m to the seabed) into 150-m and 300-m survey lengths and complete across-channel transects and exported them with summed wedge volume, total number of fish tracks, and mean geographic position and time per partition. We estimated fish density (fish/1000 m3) for each 150-m and 300-m survey length and complete across-channel transect by dividing the total number of fish tracks by summed wedge volume. The spatial distributions of fish densities were visualized in geographic information system software .We gridded fish track detections into 1-m range bins and exported with summed wedge volume17. We estimated fish abundance per survey by multiplying mean fish density by total survey volume as determined from bathymetric data and time-evolving tidal height. Standard errors and 95% C.I. of abundance were estimated by multiplying the bootstrap-estimated values for density by the volume of surveys. Autocorrelation analysis was conducted to ensure statistical independence between across-channel transect densities and the unbiased estimation of variance17. However, for systematic surveys with strong spatial trends in fish densities , variance can be biased high, potentially warranting post-stratification of data to mitigate variance inflation60; this potential bias was not evaluated in this study. We tested that mean density on incoming tides was lower than outgoing tides with a one tailed t-test (α = 0.05) after the data were tested for homoscedasticity with Levene’s test (α = 0.05) and normality with an Anderson-Darling test (α = 0.05).Mean fish density per survey was calculated by a transect-volume weighted average of across-channel transect densities. Standard errors, coefficients of variation, and 95% confidence intervals (95% C.I.) of survey densities were estimated from bootstrap resampling  of mean density of across-channel transect densitiesTS of fish tracks with at least three consecutive measures to increase the probability of detections with multiple incidence angles. We converted mean TS of fish tracks to L using the derived KRM-TS versus L equation, and mean L per survey was calculated. We converted mean L to mean weight using a length-to-weight relationship previously estimated for Corvina50 and multiplied by total abundance to estimate spawning stock biomass per survey. Variances of mean L and abundance were propagated and summed in quadrature as fractional uncertainties to estimate the standard errors and 95% C.I. of biomass.To estimate mean fish length and biomass for each survey, we isolated mean p2; Pa2) over the 251–498 Hz band was measured by integrating the pressure spectral density  across the peak frequency bandwidth of Corvina chorusing  to visualize the spatial and temporal distribution of sound production and their relation to fish densities. We tested that mean p2 on the incoming tide was lower than on outgoing tides with one-tailed t-test (α = 0.05) after the data were tested for homoscedasticity with Levene’s test (α = 0.05) and normality with an Anderson-Darling test (α = 0.05).We visually and audibly inspected recordings of ambient sound and extracted 20-s segments free of nearby boat noise and operational disturbances. For each 20-s recording, the mean square pressure amplitude  that were nearest in space and time to each passive acoustic recording station were compared to p2 measurements separately. Measurements of ρ and p2 that were not coupled in space and time were excluded from comparisons. We generated and examined plots of p2 vs. ρ as a function of time in relation to high tide to test our hypotheses that a relationship with either strong  to observe the evolving relationship between sound production and fish density. We generated regressions between p2 vs. ρ for each hour using generalized linear model (GLM) regressions. We compared regression slopes using analysis of covariance  to test for homogeneity and through multiple comparisons  to identify the time period with a stable relationship between p2 and ρ. We combined hour periods with homogeneous regression slopes to test our hypotheses and construct a model (GLM) to estimate ρ from p2. Results from complementary analyses using densities over 150-m and 300-m survey lengths were compared and tested for homogeneity using analysis of covariance  and found to not be significantly different; thus, comparative results are only presented for modeled relationships between p2 and ρ per 150-m survey length (see Supplementary Figs Fish densities per 150-m and 300-m survey length (Supplementary Information for Rowell et al. Estimating fish abundance at spawning aggregations from courtship sound levels


Saccharomyces cerevisiae strains (flor yeasts) form a biofilm (flor velum) on the surface of Sherry wines after fermentation, when glucose is depleted. This flor velum is fundamental to biological aging of these particular wines. In this study, we identify abundant proteins in the formation of the biofilm of an industrial flor yeast strain. A database search to enrich flor yeast “biological process” and “cellular component” according to Gene Ontology Terminology (GO Terms) and, “pathways” was carried out. The most abundant proteins detected were largely involved in respiration, translation, stress damage prevention and repair, amino acid metabolism , glycolysis/gluconeogenesis and biosynthesis of vitamin B9 (folate). These proteins were located in cellular components as in the peroxisome, mitochondria, vacuole, cell wall and extracellular region; being these two last directly related with the flor formation. Proteins like Bgl2p, Gcv3p, Hyp2p, Mdh1p, Suc2p and Ygp1p were quantified in very high levels. This study reveals some expected processes and provides new and important information for the design of conditions and genetic constructions of flor yeasts for improving the cellular survival and, thus, to optimize biological aging of Sherry wine production.Several  Saccharomyces cerevisiae flor yeast strains are very interesting for winemaking purposes due to their influence on the sensory properties of Sherry type wines [pe wines ,2. TheseDuring biological aging, flor yeasts develop an oxidative metabolism (respiration) under harsh survival conditions, including high ethanol and acetaldehyde concentrations, oxidative stress and low pH. Buoyancy and spontaneous, natural immobilization by formation of a biofilm (flor velum) on the surface of wine provide an effective mechanism that protects the cells and facilitates survival and proliferation under the adverse conditions during the biological aging. In fact, the formation of a biofilm at the wine-air interface brings yeasts into contact with an oxygen-rich zone where they can efficiently metabolize ethanol and glycerol. Yeast proliferation under the prevailing oxidative conditions of biological aging is also facilitated by the antioxidant defense system protecting cells from reactive oxygen species (ROS) formed during the oxidative metabolism of non-fermentable carbon sources (ethanol and glycerol) ,4.The ability of flor yeasts to float on the surface of wine was initially ascribed to a high surface hydrophobicity of yeast cells  resultinSaccharomyces Genome Database) and UniProt databases in order to elucidate the overall proteome of the flor yeast strain. Lastly, the GO Term finder tool of SGD and a GO Term or pathway ratio were utilized to identify the GO Terms, pathways and particular proteins exhibiting the greatest differences between the two tested conditions.This study is part of a sequence of proteomic research that was focused on specific flor yeast organelles/processes ,10,11,12A total of 413 proteins was detected under the BFC and 611 under NBFC, of which 266 were more abundant under BFC . On the other hand, 439 proteins were more abundant under NBFC . A total of 105 proteins was found in similar amounts under BFC and NBFC. Average protein content in mol % was 0.24 in the case of BFC and 0.16 in NBFC.The roles of the most abundant proteins under each type of condition were elucidated from the GO Term or pathway ratios for cellular components, biological processes and pathways see . Mutuallp-value < 0.001 considering BFC more abundant proteins); one (Mdh1p) was present at 1.02 mol % under BFC, but absent under NBFC. This result is consistent with a balanced cellular redox process in flor yeasts [A large number of proteins present in cellular components, biological processes and pathways participating in cellular respiration metabolism were detected under BFC. Biofilm-forming flor yeasts are known to have an oxidative metabolism , also cor yeasts , where MAs can be seen from S. cerevisiae were detected under BFC and two under NBFC  can be severely damaged by the presence of reactive oxygen species produced by the respiratory chain under biological aging conditions. In fact, ROS are reported as more deleterious on mtDNA than they are on nuclear DNA, probably as a result of the greater proximity of the former to the major sites of the endogenous production of ROS . In thisder NBFC . NADPH ip-value < 0.1) were also detected in increased amounts under BFC  in der NBFC . Hsp82p,der NBFC .The facts that the total number of proteins detected was greater under NBFC (611) than under BFC (413) and that 80% of the proteome is expressed during the log phase (14) led us to expect a greater number of abundant proteins involved in translation functions under NBFC. Thus, we may hypothesize that protein biosynthesis becomes important in the oxidative environment provided by the typically high ethanol concentrations under BFC, where flor yeasts are active in replacing proteins damaged by ROS and/or a high ethanol content of the medium ,22,25. Tp-value lower than 0.1. Elevated frequencies of more abundant proteins and specifically cell wall proteins contributing to stress responses were reported under BFC; for instance, Ecm33p, which is specifically involved in the response to oxidative stress and ethanol [Cell wall is a structure that affords protection from stresses and contributes to cell morphogenesis. The more abundant proteins under BFC annotated in the fungal-type cell wall showed a  ethanol ,28, and  ethanol .Besides the previous functions, cell wall proteins have been associated with the velum-forming ability of flor yeasts. According to some authors, the presence of molecules such as β-glucans or mannoproteins, which increase the hydrophobicity of cell surfaces, affects such ability ,30,31,32d-glucan chain elongation and intrachain 1,6-β linkages into 1,3-β glucan; the latter contributes cell wall structural rigidity [N-glycosylated GPI-anchored protein also contributing to cell wall integrity and correct assembly of the mannoprotein outer layer of cell walls. Scw10p and its paralogue Scw4p, which was present under both BFC and NBFC, albeit in markedly different amounts (>2-fold under BFC), are two glycosylated glucanases. Scw4p synthesis has been associated with increased adhesion [scw4 scw10 double mutant to exhibit defects in mating. Hence, high contents of both proteins under BFC may indicate that flor yeasts are undergoing sexual reproduction.As can be seen from the rigidity ,38. Ecm3adhesion  and hencadhesion  found thN-glycosylated cell wall-related secretory protein potentially involved in cellular adaptations prior to the stationary phase, cellular amino acid metabolism, cell wall assembly and, also, possibly, biofilm formation [YGP1 induction in response to nutrient limitation and repression by high glucose concentrations. Based on the large differences in contents between BFC and NBFC, its functions and the fact that until now it has only been located in the extracellular region [Nineteen cell wall proteins were also detected in the extracellular region, which contained several others not present in cell walls . Suc2p, Ygp1p and Yor389wp were present in greater amounts under BFC than under NBFC; the former two at up to 0.97 mol % and 1.12 mol %, respectively. Suc2p catalyzes the hydrolysis of molecules such as fructan, raffinose, inulin and sucrose, none of which were present in the studied media. Its unexpectedly increased contents under BFC may have resulted from the yeasts responding to a medium containing no fermentable carbon sources. By contrast, Ygp1p exhibited the largest differences between BFC and NBFC. Ygp1p is an extensively ormation . Destrueormation  observedr region , Ygp1p mr region . A high r region .S. cerevisiae deletion mutant bank and suggested that the peroxisome also contributes to biofilm formation. In this work, we detected six different peroxisomal proteins under BFC, all of which were more abundant than under NBFC . Finally, Pnc1p catalyzes nicotinamide de-amidation, an early step in the NAD+ salvage pathway potentially related to the response to oxidative stress. Two other proteins that were only detected under BFC (Cit2p and Idp3p) participate in the glyoxylate cycle, which is essential for growth on two-carbon compounds. Cit2p also participates in the TCA cycle. On the other hand, Idp3p may act in the production of NADPH for fatty acid and sterol synthesis, which is important for flor yeast flotability [Vandenbosch et al.  performeder NBFC . Pex1p, ormation  and decrormation ,45. Two tability . Pox1p, S. cerevisiae vacuolar lumina were present under both conditions .The vacuolar proteins detected under BFC were highly expressed . The combined contents in vacuolar proteins were up to 10-times higher under BFC than under NBFC. Three of the eleven proteins detected in nditions . Two sucS. cerevisiae were detected under BFC. In response to hydrogen peroxide, Pep4p migrates outside into the cytoplasm to mediate mitochondrial and nucleoporin degradation [S. cerevisiae were detected under BFC, with seven in greater contents than under NBFC ; on the other hand, six proteins were detected under NBFC, three in greater amounts than under BFC  were detected under both BFC and NBFC and a third (Bmh2p) under BFC only  were detected under BFC, but only one (Ypr1p) was under NBFC in a lower content than under BFC. Since neither sugar was present in the studied media, these proteins may be involved in a different process. In fact, all are somehow associated with oxidative stress response and adaptation [As can be seen in aptation . Thus, Gaptation ,56, wheraptation . AlternaS. cerevisiae; three were more abundant under BFC , and so was the other (Gcv2p) under NBFC  were present in greater amounts under BFC; three  were found in higher contents under NBFC; and the other (Ilv5p) was detected in similar amounts under both conditions. Bat1p, with 0.47 mol % under BFC and 0.09 mol % under NBFC, is known to be highly expressed during the logarithmic phase of growth and downregulated during the stationary phase. Mmf1p, which was detected in increased amounts under BFC (0.52 mol %), is associated with mitochondrial translation and to mitochondrial DNA maintenance. On the other hand, the isoleucine degradation process was also highlighted  and a D isomer, which acts as a carbon, nitrogen and energy source. A total of ten proteins related to isoleucine synthesis in hlighted : four prhlighted . Anotherompounds .S. cerevisiae; fifteen were detected under BFC and fourteen under NBFC . Ade3p, Shm1p and Shm2p specifically play a role in the folate polyglutamylation. This compound contains healthy properties and is used as a food additive because humans are unable to synthesize it ,64. Due Most of the proteins located in organelles or involved in biological process/pathways associated with cell division and gene transcription were detected in increased amounts or even exclusively under NBFC see . The thiS. cerevisiae flor yeast at the early stages of biofilm formation exerts oxidative metabolism and produces a protein machinery that prevents and protects the yeast from reactive oxygen species (ROS) derived from respiration . Previously, samples were passed on 0.01 M of an EDTA solution and then counted in order to avoid cell flocculation. Cell viability was determined by spreading 100-µL volumes of diluted suspension onto YPD agar plates and counting colonies after 48 h at 28 °C.Data treated in the present manuscript were extracted from Moreno-García et al. , where tMethods for the collection of the cells and protein extraction, fractionation, identification and quantification are indicated in Moreno-García et al. ,10.p-value for each annotation; this is the probability or chance of seeing at least “x” number of genes out of the total “n” genes in the list annotated to a particular GO term, given the proportion of genes in the whole genome that are annotated to that GO Term. GO Terms with p-values lower than 0.1 have been distinguished through the manuscript. Further, it should be noted that some proteins may be present in more than one cell compartment or exhibit more than one molecular function; and involved in more than one process as a result. Moreover, the false discovery rate (FDR) has been also determined through the GO Term finder. This is obtained by making 50 simulated runs with aleatory genes and counting the average number of times a p-value as good as or better than a p-value generated from the real data is seen.An explanation for the overall proteome was sought by performing a GO Term and pathway search for the most abundant proteins , using the SGD YeastMine tool and UniProt databases ,68. ProtThe discussion focuses on the GO Terms or pathways leading to the greatest numbers of more abundant proteins (including specific proteins and common proteins with more content in one of the conditions) gathered under BFC or NBFC relative to the other condition and is based on the GO Term or pathway ratio, which was calculated as follows:The GO Terms and pathways with a ratio higher than 2 were assumed to play an important role under BFC and the one’s with ratio lower than 1/2 to be important under NBFC. By contrast, those GO Terms gathering only 1–2 proteins under either condition against 0–1 under the other were excluded as they might have led to erroneous conclusions.


The tribal population of Telangana, India, lives in remote and difficult conditions. This study was carried out to find out estimate, the prevalence and progression of disability in elderly population among tribals of Khammam District, Telangana state, India.A population based cross sectional survey was conducted in villages of Tribal Sub Plan area. Elderly people who are 60 years or older were chosen with a two stage sampling procedure: (1) probability proportion to size was used to select clusters and (2) in each selected cluster households were selected by systematic random sampling. The participants were interviewed with the 36 item Telugu version of the World Health Organization Disability Assessment Schedule (WHODAS 2.0) questionnaire. Socio- demographic information, behavioral measurements, health and social benefit indicators were also assessed. Descriptive analytical methods were used for prevalence estimation and logistic regression was used to examine the associations of progressive age over disability among elderly.A total of 506 elderly people from 1349 households in 20 villages across 31mandals of Khammam were interviewed. Majority of elderly population among tribals were illiterate , used tobacco , consumed alcohol  and were hypertensive . The prevalence of disability was higher in women. Maximum disability in the interviewed elderly population was seen in domains of performing house hold activities, and mobility. In comparison with men, women expressed more disability for majority of domains. As age progressed, the disability for self-care domain increased to a maximum of 2.6 times in men and 6.6 times in women and for mobility domain increased to a maximum of 9.7 times in men and 7.2 times in women.Although present disability modifying mobility Assistive Devices (AD) can help elderly in overcoming disability, these are primarily designed for built environments. As the needs, cultural sensitivities, and living environment of elderly population in tribals are unique, newer innovative assistive devices should be designed and developed. The world’s population is ageing rapidly. People aged 60 and above are defined as elderly and the population of global elderly is projected to increase from 12.3% at present to 22% by 2050 , 5. DisaAssessment Schedule-version 2.0 (WHODAS 2.0) questionnaire based assessment tool have shown that majority of elderly people have at least one or more ADL limitation.Newer understanding and newer approaches may be necessary as elderly disabled people require significantly more health and social care resources to maintain acceptable quality of life. Health systems of several countries have already begun their refocus , 8.The new state of Telangana was formed in 2014 by the reorganization of Andhra Pradesh, one of the largest states of India; in response to sustained agitation demanding improved, equitable development and better employment. Improving the lives of its 9.34% tribal population as per 2011 census) . Economi census .The ten existing districts of Telangana have further been re-organized into thirty one districts  and imprThe first objective of this study was to estimate the prevalence of disability in terms of activity limitations and participation restrictions in elderly population among tribals. The second objective was to find out whether the disability was progressive with age and to quantify the progression. According to the 2011 Census, with in the state of Telangana, Khammam District had the highest percentage of Scheduled Tribes at 27% . The disA cross sectional population based survey was conducted among elderly population aged 60 years and above, in the TSP area of Khammam District, Telangana state. Two stage cluster sampling was used, in the first stage, all 904 villages of the TSP area were listed and 125 villages that have population of 500 and above and with 80% tribal population were selected. From among the 125 select villages, 20 clusters were randomly selected using probability proportion to size (PPS) sampling method. Each village was considered as a cluster for operational purposes. In the second stage, 72 households were selected from each selected cluster by using systematic random sampling method. Considering, 22% as prevalence of disability in elderly , a relatAs the objective of this study was to assess daily activity limitations and broader social participation restrictions of elderly individuals irrespective of clinical disability status, the WHO DAS 2.0 (Disability Assessment Schedule) 36-item version interviewer administered questionnaire was used. WHO DAS 2.0 Questionnaire was translated into local language i.e. Telugu, by a professional translator and the same was back translated into English by another independent translator by using WHO translation and back translation protocol . The TelWHO DAS 2.0 (36 item)  assessesWHO STEPwise approach to Surveillance (STEPS) questionnaire was used to asses risk factors for general state of health. The interviewer administered assessment questions can be grouped in to three focus areas: (1) Socio demographic data including age, gender, marital status, education and living arrangements; (2) behavioral measurements like tobacco and alcohol use and; (3) assessment of health indicators like self-rated health and hypertension. In addition, information on social benefit indicators like visit by health worker and receipt of old age pension from government were collected from self-reported responses to interviewer administered survey modelled on WHO STEPS questionnaire . Anthrop2 Overweight: 23.0–24.9 kg/m2, Obesity: >25 kg/m2) [All elderly people who were interviewed were weighed without foot wear, using an electronic weighing scale with an error range of ±100 grams. The weighing scale was regularly checked with known standard weights. A portable Stadiometer was used for measuring height, nearest to 1 cm, using standard procedure. Body Mass Index (BMI) was calculated as weight in kilograms divided by square of height in meters. Overweight and obesity was defined as BMI of ≥ 23 and ≥ 25 respectively  progression with each item on the six domains of WHO Disability Assessment Schedule 2.0. Logistic regression was considered the appropriate statistical model because the dependent variables (difficulty experienced “yes” or “no”) were categorical and dichotomous. The estimated associations of age category with individual items on the six domains of WHODAS 2.0 are presented as odds ratios. The youngest age category of 60–64 years considered as reference and compared with older age category of 65–69 years and the oldest age category of 70 years and above. Significance level of 95% was considered for all estimations.Table The distribution of disability as domain specific functional limitation or difficulty in activity responses across age categories and gender are presented in Table Table Disability is not a binary condition, but a multi-faceted, multi domain functional limitation. From this study it is evident that disability related functional limitations like mobility and performing house hold activities are more impaired in elderly tribal population than other limitations like cognition, self-care, getting along with people and social participation. Women comparatively reported higher disability across most domains of disability. Also it was evident that disability related functional limitations are highly progressive, however gender variation was not statistically significant. Similar studies reported similar results of higher functional disability in women ; higher Any discussion addressing the issue of disability related functional limitations of elderly population among tribals should be cognizant of the difficult environment that is predominantly open than built, and that of extreme resource and literacy constraints. For elderly with mobility limitations, difficult environment are associated with the majority of falls  and a foThis study highlights the fact that elderly population in tribal areas, much like the rest of the elderly population are affected by different domains of functional limitations and it is progressive. The needs, cultural sensitivities, and living environment of tribal population remain unique. Changing political landscape, the promise of better life, newer innovations, accessible and user friendly technologies should all converge around the needs of the poor and suffering elderly population among tribals. Along with the provision of regular monthly pension of INR 1500 to all elderly disabled people in tribal areas, the Government of Telangana should also support a research based complimentary process of understanding the local life, needs, local environment and means of communication beyond traditional methods to improve the life of elderly population living with disability in tribal areas.