Salmonella spp. and yeasts/molds. The bacterial and fungal isolates were identified using phenotypic and genotypic methods and the study of the production of mycotoxins was carried out from the fungal isolates. In collected samples, the TAMF count ranged from 0 to 1.8 × 106 CFU/g with a total average of 6.3 × 104 CFU/g. About 2% of the samples had a microbial load exceeding the standards (105 CFU/g). No Salmonella spp. was isolated in the final infant flours. However, the presence of Enterobacteriaceae (Klebsiella spp. Enterobacter spp. and Cronobacter spp.) was detected and molecular characterization revealed also the presence of fungal species of the genus Aspergillus spp., Penicillium spp. and Fusarium spp. Some of these species were found to produce aflatoxins, ochratoxin A and fumonisins, which are potential carcinogenic toxins. These results demonstrated the need for a preventive approach based on the application of Hazard Analysis Critical Control Point in the food industry to ensure food safety of infant flours in Burkina Faso.The prevalence of diarrheal diseases in children aged from 6 to 24 months in Burkina Faso is 38%. These diarrheas may be due to the consumption of contaminated weaning food. Therefore, the microbiological quality of follow up infant flours used as supplement foods is a key-point to reduce children diseases. In this study, the microbiological safety of locally-produced infant flours was investigated. One hundred and ninety-nine (199) samples were collected mainly in retails outlets and in Recovery and Nutrition Education Centers. According to the Burkina Faso regulations, microbiological analyses were carried out for Total Aerobic Mesophilic Flora (TAMF), thermotolerant coliforms,  A molecular identification of the isolated fungal strains and a verification of their production of toxins were performed.The agri-food sector is growing up in Burkina Faso with emphasis on the commercialisation of local food products for young children. According to the Ministry in charge of food safety in Burkina Faso 2.2.1.This study was conducted in Ouagadougou, capital of Burkina Faso. About 199 samples of infant flours were collected from June 2013 to December 2014 in the Recovery and Nutrition Education Centers (RNEC), semi-industrial units and artisanal units . Approxi2.2.Microbiological analysis was carried out in accordance with the requirements of the International Organization for Standardization (ISO). The stock solution was obtained according to ISO 4833:2003. Ten grams (10 g) of infant flours were collected aseptically and placed in a 100 mL flask containing 90 mL of sterile Buffered Peptone Water (BPW). The mixture was homogenized with a stirrer and diluted using successive decimal.TAMF was determined according to the AFNOR method, ISO 4833:2003. One hundred microliters (100 µL) of each dilution were used to inoculate in duplicate on Petri dishes containing Mueller Hinton medium  and incubated in an incubator at 37 °C. Colonies were counted on the plates after 18 h to 24 h of incubation.The thermotolerant coliforms were determined by the AFNOR method, standard V08-017:1980. The seeding of 100 µL by spreading was performed in duplicate on Petri dishes containing Eosine Methylene Blue Levine medium  and incubated at 44 °C for 24 h. Characteristic bluish colonies with a dark brown center, flattened, which occasionally have a metallic luster were counted. These characteristic colonies were transplanted onto MH agar and incubated at 37 °C for 24 h and then stored in cryotubes containing 40% glycerol at −20 °C. These cryotubes were later used for identification using API 20E kits .Salmonella were determined using AFNOR method, ISO 6579:2002. A pre-enrichment was carried out in buffered peptone water  by incubating the decimal dilutions at 37 °C. After 18 h of incubation, 1 mL of this pre-enrichment was inoculated in 9 mL of Rapaport-Vassiladis  and incubated at 37 °C for 24 h. One hundred microliters (100 µL) of the enriched broth were used to inoculate Petri dishes containing Hektoen Enteric Agar  and incubated at 37 °C for 24 h. Suspended green to green-blue colonies with or without black center were transplanted onto Xylose Lysine Decarboxylase  and incubated at 37 °C for 24 h. Then the characteristic colonies were counted.Yeasts and molds were determined by the AFNOR method, ISO 7954:1987. Thus 100 microliters (100 µL) of each dilution were seeded in duplicate by plating on Petri dishes containing the Sabouraud + chloramphenicol 0.5 g/L medium  and incubated at 25 °C for 3 to 5 days. Yeasts and molds were enumerated and interesting mold strains were stored in cryotubes containing 40% glycerol at −20 °C. These molds were used later to test the production of fungal toxin and to carry out the molecular characterization of the strains.2.2.1.Enterobacteriaceae strains. They consist of various miniaturized biochemical tests in which the inocula have been deposited. After 24 h at 37 °C, the tests were read and the Enterobacteriaceae were identified using BioMérieux software.BioMérieux's API®20E identification galleries are test kits for identification of 2.2.2.The fungal strains conserved in glycerol were grown on Potato Dextrose Agar medium  at 25 °C for 7 days, in order to carry out macroscopic and microscopic identification using morphological criteria.2.3.2.3.1.CTAB solution consists of 20 g of CTAB, 100 mL of Tris pH 1 M, 280 mL of 5 M NaCl and 40 mL of 0.5 M EDTA (Ethylene Di-amine Tetra-acetic Acid). For working solution 40 g of polyvinylpyrrolidone and 5 mL of b-mercaptoethanol were mixed for 1 L of solution called CTAB. CTAB solution was autoclaved and the vial wrapped with aluminum foil.4OAc 7.5 M. This mixture was centrifuged for 5 min at maximum speed. The supernatant was removed and 500 µL of cold ethanol (70%) was added to the pellet. This new mixture was centrifuged at maximum speed for 5 min. The supernatant was gently removed and the pellet was allowed to air dry. The whitish pellet was re-moistened in 20 µL of sterile pure water (Millipore) for carrying out the PCR amplification.Thus, the mold colony was picked on a Petri dish with 50 µL of Triton-X 100 and mixed in a 1.5 mL eppendorf tube containing 500 µL of CTAB working solution and sterile glass beads (Sigma G9772). This mixture was vortexed (Vortex Genie 2 T) for 2 min, then the tube was immersed in a water bath at 65 °C for 15 min. Then 500 µL of Chloroform: isoamyl alcohol  was added to the eppendorf tube and gently agitated before passing it to the centrifuge at a maximum speed of 6000 rpm for 5 min. Four hundred (400 µL) of the upper (aqueous) phase was transferred to a new eppendorf tube containing 233 µL of isopropanol and 32 µL NH2.3.2.The purified genomic DNA was amplified by PCR with primers ITS 1 and ITS 4 (the PCR reaction medium is summarized in 2.3.3.Sizes of the amplified DNA fragments were controlled by electrophoresis by deposits of 5 µL on 2% agarose gel with TAE buffer (Tris-Acetate-EDTA). PCR products were conditioned and sent to GATC laboratory  for sequencing. Sequencing data were edited manually using BioEdit software. The sequences were then compared to the databases using BLAST algorithm. The identified sequences corresponding to the high scores and e-value were chosen to assign the identification.2.4.Aspergillus spp., Penicillium spp. and Fusarium spp. were transplanted onto Potato Dextrose Agar , and then incubated at 25 °C for 5 days.The strains identified as Aspergillus spp., Penicillium spp., edge of fungal biomass agars plates were put on petri dishes and placed in a 4 mL vial. Two point five mL (2.5 mL) of a methanol: formic acid mixture  was added to the biomass of Aspergillus spp. and Penicillium spp. The extracts were immersed in an ultrasonic bath for 20 min before being filtered. The filtrates obtained were collected into a glass bottle, identified by High Performance Liquid Chromatography (HPLC) and quantified by spectrofluorescence  after post column derivatization with electrochemical system . Fluorescence detection for AFs was set at 365 nm excitation and 435 nm emission and OTA was set at 333 nm excitation and 460 nm emission. The mobile phase A was water-methanol , 119 mg of potassium bromide and 350 µL of nitric acid and the mobile phase B was water methanol , 119 mg of potassium bromide and 350 µL of nitric acid. AFs and OTA standard solutions were used for the construction of a five-point calibration curve of peak areas versus concentration (ng/mL). The operating conditions were as follows: injection volume of 100 µL of sample and standard solutions; C18 reverse-phase HPLC column, Uptisphere type, ODS, 5 µm particle size, 5 ODB, 250 × 4.6 mm, with identical pre-column, thermostatically controlled at 40 °C; isocratic flow rate of 0.8 mL/min. Mobile phase gradient: mobile phase A: 0% (0–26 min); 65% (26–45 min); 0% (45–50 min); 41% (20–25). The detection and quantification limits on aflatoxins were 0.3 µg/kg and 1 µg/kg, respectively. The detection and quantification limits on ochratoxin A were 0.05 µg/kg and 0.1 g/kg, respectively. The contents were calculated from a calibration curve established with aflatoxins  and ochratoxin standards .For strains potentially OTA and aflatoxins producers, i.e. Fusarium spp., 2 mL of a methanol:water  mixture was added to the biomass of Fusarium spp. The extracts were immersed in an ultrasonic bath for 20 min before being filtered. The filtrates obtained were collected and derivatized with O-phthaldialdehyde (OPA) prior to analyze by HPLC and quantified by spectrofluorescence . Fluorescence detection for fumonisins was set at 335 nm excitation and 440 nm emission. The mobile phase A was: acetonitrile-acetic acid , and the mobile phase B was water acetic acid . The derivatized sample was prepared as follow: 100 µL of eluate was mixed with 100 µL of OPA. The operating conditions were as follows: injection volume of 100 µL of derivatized sample; C18 reverse-phase HPLC column, Uptisphere type, ODS, 5 µm particle size, 5 ODB, 250 × 4.6 mm, with identical pre-column, thermostatically controlled at 35 °C; isocratic flow rate of 1 mL/min. Mobile phase gradient: mobile phase A: 41% (0–9 min); 61% (9–16 min); 100% (16–20 min); 41% (20–25 min). The detection and quantification limits were 5 µg/kg and 20 µg/kg, respectively. The contents were calculated from a calibration curve established with fumonisin standard solutions .For fumonisin-producing strains, i.e. 2.5.After the colonies counting on Petri dishes, the result was expressed in Colony-Forming Unit (CFU)/g of sample, according to the Moroccan standard ISO 7932 as follows:If the boxes contain 150 to 300 colonies: If there are less than 15 colonies on the boxes, Y is the arithmetic mean of the colonies counted on the 2 Petri dishes. Salmonella which was interpreted according to the 2 class plan described by the Government of Canada “Lignes directrices et normes pour l'interprétation des résultats analytiques en microbiologie alimentaire”. Analytiques methodes accredited ISO/CEI 17025 by the standards council of Canada (N°131).Interpretations were made according to the 3 class plan except The data was recorded with Microsoft Excel 2007 and the ANOVA were performed with XLStat PRO version 7.5.2. Interpretation of the values was carried out according to the Fisher test, the least significant difference (LSD) with a confidence interval of 95% between the UFC/g number of food and the origin of the product.3.Infant flours from cereal bases are preparations in the form of porridge that are given to children from six months of age as a complement to breast milk. They must contain nutrients for the growth of young children. According to the preparation, there are several types of infant flours: flours to cook, pre-cooked flours, cooked or instant flours At the level of RNEC and artisanal units, the infant flours produced were flours to cook and were manufactured in an artisanal way . These infant flours were composed of local cereals , oilseeds , sometimes legumes (sweet potatoes), sugar, milk, oil or salt.At the level of semi-industrial units, which are small and medium-sized enterprises with adequate industrial equipment, infant flours consist in local cereals , certain oilseeds , legumes (tubers), sugar, milk but also certain additives such as mineral supplements, vitamins and industrial alpha-amylolytic enzymes. These semi-industrial units produce both types of infant flours. With the exception of those produced in the RNEC, the sales of infant flours mainly takes place in supermarkets, in the Center for Health and Social Promotion (CHSP) and at the place of production.3.1.The results of the microbiological analysis were compared with the standards reported by Gret/Orstom In this study, the microbiological analysis of the flours to cook revealed that the majority of the samples (92.1%) were satisfactory compared to the microbiological food safety standards for infant flours .The results of the microbiological analysis of the instant flours revealed no unsatisfactory sample for both TAMF, thermotolerant coliform and yeast/mold .5 and 3.5 × 104 CFU/g according to the production site.As shown in 6 and 2.6 × 105 respectively. It was 13.8% for the infant flours produced by the semi-industrial units with CFU values of 9.8 × 105 slightly lower than those obtained by the artisanal units and slightly higher than those obtained by the RNEC.The statistical analysis indicated that there was no significant difference according to the sites of production. However, 6 in the infant flours that were produced by artisanal units. Though educational training was organized by various sanitary authorities in these units, a permanent and effective monitoring of the good practices was lacking. The emphasis was rather on the income generated by the production of these infant flours. In semi-industrial units, where monitoring for good hygiene and manufacturing practices were assumed to be stricter, we still obtained up to 13.8% of unsatisfactory infant flour samples because of lack of good practices. Yet the most part of semi-industrial units were followed by Gret with the Nutrifaso project which aimed to improve the quality of food for the children (6 to 24 months). Several works have been carried out in order to implement good production procedures The number of unsatisfactory samples was lower in flours to cook but the results were unsatisfactory with regard to good manufacturing practices Salmonella spp. is a potential source of contamination after heat treatment Cronobacter spp. was more commonly found than Salmonella spp. in infant flours 3.2.3.2.1.Klebsiella (K. pneumoniae and K. planticola), 35% (7/20) to the genus Enterobacter (E. cloacae and E. gergoviae) and 5% (1/20) to presumptive Cronobacter spp. According to the Public Health Agency of Canada the species belonging to the genera Klebsiella spp. and Enterobacter spp. are commensal bacteria in the human and animal digestive tract. Thus, they may be an indicator of fecal contamination Klebsiella were involved in many cases of sepsis, urinary and wound infections, notably in intensive care units and neonatal sepsis. According to FAO/WHO, strains of the genera Cronobacter are opportunistic pathogens Cronobacter spp. is not well understood, however, they have been isolated from a diverse range of environments, e.g. processing plants and foods or powdered infant formula Cronobacter spp. also have been isolated from a wide range of foods other than powdered infant formula, including various types of meat, fish and fish products, herbs and spices, cereals and fermented bread Cronobacter spp. was implicated in outbreaks of meningitis and enteritis, especially in infant and young children. A reduction in the prevalence of Cronobacter has to be obtained by improving hygiene with HACCP, GMP and GHP Identification by Api 20E kits indicated that 60% (12/20) of the isolated strains belonged to the genus 3.2.2.Aspergillus spp., Penicillium spp. and Fusarium spp.) and various other genera such as Trichoderma spp., Cladosporium spp., Geotrichum spp., Mucor and Paecilomyces spp. could be identified. The genus Aspergillus spp. was the most prevalent in infant flours with a percentage of 69.7%, followed by Penicillium spp. with a percentage of 10.5% and finally Fusarium spp. with a percentage of 3.3%. The various other genera identified accounted for 5.9%. It should also be noted that nearly 10.5% of the strains isolated could not be identified by this method. Other authors have also found an abundance of the genera Aspergillus spp., Fusarium spp. and Penicillium spp. in corn flour samples. The proportions were 93.3% of Aspergillus spp., 83.3% of Penicillium spp., 53.3% of Mucorales, 43.3% of Cladosporium spp., 33.3% of Fusarium spp. The remaining species recorded in less than 20% of the samples were Trichoderma spp., Beauveria spp., Geotrichum spp., Alternaria spp., Paecilomyces spp., Acremonium spp. and Scopulariopsis spp. One hundred and fifty (150) fungal strains were isolated from infant flours. Depending on their morphology (macroscopic and microscopic) 3 main genera .After isolation, PCR amplification and sequencing of the 28S DNA region of fungal strains, the data were compared with the National Center for Biotechnology Information (NCBI) DNA database in order to obtain the molecular identification of isolates. Concerning the genus 3.3.Despite of the measures to reduce the presence of contaminants, mycotoxins have been detected in infant flours A. flavus and A. carbonarius were able to produce notable amounts of mycotoxins (aflatoxins and ochratoxin A respectively)  . It can Mycotoxin-producing species in infant flours can lead children to develop serious chronic diseases such as cancer and immunosuppression These results show that infant flours used as a food supplement are rather potentially risky foods for health. They could lead to undernutrition or chronic diseases. Adopting good agricultural practices in seed, harvest, post-harvest, and treatment could reduce mycotoxin contamination in the first place.4.Depending on the microorganisms sought in the infant flours, high rates of contamination have been observed in artisanal units. Even though overall coliform and mold contamination levels are acceptable in infant flours, the presence of microorganisms, species or toxins can pose a great hazard to the consumer, especially for a child whose immune status is still fragile. The bacterial and fungal species present in infant flours have shown that they can either alter the food or cause acute or chronic diarrheal diseases thus weakening the child health. This could put a high risk on the actions taken so far to combat malnutrition. Thus, the danger is now known. This will involve putting in place prevention mechanisms that will reduce the microorganisms present in infant flours, especially in the case of mold toxins. Further research is thus needed to find innovative solutions to address overlooked issues of aflatoxin contamination and exposure. Finally, the collection of additional information could be useful for a number of policies makers and the development of regulations on mycotoxins in foods.


To analyze hospitalization episodes with an ICD-9 diagnosis code of influenza (codes 487 and 488) in any diagnostic position from 2009 to 2015 in the Spanish hospital surveillance system.Information about age, length of stay in hospital, mortality, comorbidity with an influenza diagnosis code between 1 October 2009 and 30 September 2015 was obtained from the National Surveillance System for Hospital Data .52,884 hospital admissions were obtained. A total of 24,527 admissions corresponded to diagnoses ICD-9 code 487 (46.4%), and 28,357 (53.6%) corresponded to ICD-9 code 488. The global hospitalization rates were 8.7 and 10.6 per 100,000 people, respectively. Differences between the two diagnostic groups were found for each of the six analyzed seasons. The diagnostic ICD-9-CM 488, male gender, and high-risk patients classified by risk vaccination groups showed direct relationship with inpatient hospital death.Influenza diagnosis was present in a significant number of hospital admissions. The code used for diagnosis (ICD-9-CM 488), male sex, age groups and associated risk clinical conditions showed a direct relationship with inpatient hospital fatality. Influenza is an infectious disease that mainly occurs with respiratory symptoms. However, a host of variables broadens the clinical spectrum, which ranges from mild forms to other, more serious hospital admission subsidiaries that can cause death of the patient.In 2009, the World Health Organization warned of a new influenza pandemic caused by a new type A subtype H1N1 influenza virus (A(H1N1)pdm09) . In SpaiIn this study, we aimed to assess differences of the burden of severe or complicated influenza illness and inpatient hospital fatality in each influenza season from 2009 to 2015 through the hospital discharge database. To this end, the two codes in use since 2009 for the diagnosis of influenza were considered: infections caused by influenza (ICD-9-CM 487) and those caused by certain influenza viruses (ICD-9-CM 488), mainly 2009 influenza H1N1 (ICD-9-CM 488.1). Finally, differences between the groups were evaluated.This analytical study assessed the database of the national hospital data system , which is developed annually by the Ministry of Health, Consumption and Social Welfare of Spain. The CMBD includes information on hospital discharges using a list of clinical codes to establish the diagnosis that justified the admission based on the Spanish version of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). The CMBD covers approximately 98% of public hospitals, and approximately 99.5% of the Spanish population is covered by healthcare. When necessary, the population figures obtained from the projection of the Spanish census from 2009 to 2015 provided by the National Statistical Centre were used as a denominator, and we assumed that the age distribution of the population with hospital coverage was equal to the general population.All hospital discharges over a 6-season period (from 1 October 2009 to 30 September 2015) were recorded if they included a diagnosis of influenza in any diagnostic position. The episodes were subdivided into two groups according to the diagnostic code used. The first group (group 487) corresponded to episodes of influenza (ICD-9-CM 487), and the second group (group 488) included those caused by certain influenza viruses (ICD-9-CM 488). For each record, data were collected for variables including the sex, length of hospital stay, diagnosis, and outcome. ICD-9-CM 487 mainly includes seasonal influenza, influenza A(H3N2), Influenza A or influenza not otherwise specified or not coded under another concept and constitutes a significant number of admissions in which the diagnosis of influenza may or may not have been confirmed by a laboratory test. This code also includes cases in which the doctor has decided to include the diagnosis in the discharge report, because the patient reports an episode compatible with a clinical complication of influenza infection. Conversely, ICD-9-CM 488 is defined as the diagnosis of influenza due to certain identified influenza viruses, such as the avian influenza virus A/H5N1, other influenza due to new influenza A viruses and especially the diagnosis of influenza due to influenza A virus subtype H1N1 (A (H1N1) pdm09) (ICD-9-CM 488.1). Unlike code 487, the diagnosis must be recorded as such in the clinical history for the episode to be coded as 488.1, which requires laboratory confirmation [The average number of hospitalizations per year and their age distributions, the annual incidence of hospital admissions , the average length of stay in hospital days, the mortality rate  and the Inpatient hospital fatality  were calculated. In addition, other clinical conditions described in the discharge report were analyzed and classified using the ICD-9 codes as suggested by European Centre for Disease Prevention and Control . At leasp < 0.05. The statistical analyses were performed using the statistical package SPSS for Windows, version 22.0 .In all statistical tests, the level of significance used was During the six seasons analyzed (2009–2010 to 2014–2015), 52,884 hospital discharges that included influenza code in any diagnostic position were recorded. A total of 24,527 entries (46.4%) were codified for ICD-9-CM 487 and 28,357 episodes (53.6%) for ICD-9-CM 488.p = 0.63). The global median age was 52.7 years (IR 49.6 years) without significant differences between males and females. However, in group 487, the median age was 57.3 years (IR 59.7 years), whereas patients in group 488 were significantly younger, with a median age of 49.9 years (IR 42.6 years) (p < 0.001).A total of 27,596 admissions (46.4%) were male patients, of whom 46% were in group 487, and 54% were in group 488. For females, 46.8% were in group 487, and 53.2% were in group 488. No significant differences were found in sex and its distribution by diagnostic group (p < 0.001) and are shown in Table Throughout the study period, a total of 8602 admitted patients 16.3%) were under 5 years old, and 35% were older than 65 years. These two age groups comprised the majority of inpatients in each of the diagnostic groups. In group 487, the highest percentage of admissions corresponded to the age group over 65 years of age with 41.6%, whereas in group 488 the age group over 65 years of age accounted for only 29.3% of the admissions. Group 488 had a higher percentage of patients in the 15 to 44.9-year-old and in 45 to 64.9-year-old groups than group 487. The differences were significant . Likewise, a different admission rate was observed by age group. The group younger than 5 years of age had the highest admission rates with 60.13 per 100,000 people (95% CI 58.9–61.4), and the group from 15 to 44.9 years of age had the lowest admission rates with 9.09 per 100,000 people (95% CI 8.9–9.3). Table Different hospitalization rates were also observed throughout the different seasons. The lowest hospitalization rate was observed in the 2012–2013 season with 8.82 (95% CI 8.55–9.02), whereas the highest rate of 28.58, (95% CI 28.10–29.07) was observed in the 2014–2015 season. Figure p < 0.001). The global mortality rate for the entire period was 0.90 deaths per 100,000 people (95% CI 0.87–0.94) and was highest in the group over 65 years of age with 3.08 deaths per 100,000 people (95% CI 2.93–3.24) and lowest in the group from 5 to 14.9 years of age with 0.11 deaths per 100,000 people (95% CI 0.07–0.15). Among the patients admitted, the most represented age group was over 65 years old, which reached 60% of deaths. Likewise, the group with the highest Inpatient hospital fatality was also the group over 65 years of age with 8.25 (95% CI 8.47–8.65), followed by the group 45–64.9 years of age with an inpatient hospital fatality rate of 5.41 (95% CI 5.01–5.81) and the group under 5 years of age with an inpatient hospital fatality rate of 4.81 (95% CI 4.63–4.99). Number of deaths and fatality rates relative to the age groups observed in code 487 and code 488 groups are also shown in Table A total of 2545 deaths were recorded among the inpatients throughout the entire period, corresponding to 5.2% of the admitted males and 4.4% of the admitted females p < 0.00. The gloIn addition, other clinical conditions codified during admission were analyzed and classified. The most frequent clinical condition was “Lung disease” codified in 29,797 times (56.3%) followed by “Heart Disease” in 25.5% and “Diabetes and other endocrine disease” in a 15.8%. Inpatient hospital fatality was higher in “Renal disease” (15.2%) followed by “Dementia, Stroke” in 11.9%. Frequency of each clinical condition and Inpatient hospital fatality by diagnostic group are describe in Table p < 0.001) for ICD 488, 1.71  for male gender, 2.17  for patients less than 2 years, 21.92  for patients between 2 years and 65 years with underlying clinical condition and 45.15  for patients over 65 years.A multivariate analysis was performed attending to gender and coding group. Patients were categorized in four groups based on age and underlying conditions; a) 6987 (13.2%) patients between 2 years and 65 years without underlying clinical conditions as reference group, b) 21,661 (39.8%) patients between 2 years and 65 years with underlying clinical condition, c) 5714 patients (10.8%) less than 2 years and d) 18,522 patients (35%) over 65 years. The adjusted OR for in hospital dying were 1.48 .The average inpatient length of stay was 9.68 days (95% CI 9.58–9.79), with a minimum value of 6.17 (95% CI 5.82–6.49) in the 5- to 14.9-year-old age group and a maximum of 11.86 days of stay (95% CI 11.55–12.15) in the 45 to 64.9 year old age group. The average stay was 9.02 days (95% CI 8.85–9.18) in group 487 and 10.25 days (95% CI 10.08–10.41) in group 488. The differences in the average length of stay between groups 487 and 488 by age are shown in Fig. The burden of disease due to influenza in Spain and its neighboring countries remains very high and has a clear impact on the health system, with an overload of health services during epidemic periods and a clear increase in health spending. Although most influenza cases do not require hospital admission, influenza cannot be simply considered as a mild illness. Today, we know that influenza can be a serious and even deadly disease, especially in the most vulnerable populations  and in at- risk populations.Some authors have found a low sensitivity with ICD9-CM codes for the identification of influenza cases . The senIn our study, we found significantly high hospitalization rates. The Spanish National Centre for Epidemiology (SNCE), report an accumulated hospitalization rate of severe hospitalized confirmed influenza cases (SHCIC) adjusted for age of 5.97 (5.68–6.26) cases per 100,000 population in the season 2010–2011 and 11.53 (11.09–11.99) in season 2013–2014 and a minimum of 2.58 (2.36–2.80) cases per 100,000 population in season 2012–2013 [The distribution of inpatients in the two groups was different throughout the study. As shown in Fig. Recent data suggests that seasonal influenza-associated respiratory deaths has been underestimated and has increased from 290,000 to 645,000 cases worldwide . In our In a meta-analysis, Wong et al. reviewed the risk of fatality of influenza virus A (H1N1 pdm09)  when theIn conclusion, in our study, influenza diagnosis was present in a significant number of hospital admissions. The code used for diagnosis (ICD-9-CM 488), male sex, age groups and associated risk clinical conditions showed a direct relationship with inpatient hospital fatality. For this reason, prevention strategies through vaccination with a wide coverage of groups/serotypes are an essential component to solve the problem together with epidemiological surveillance of the disease, which is the key strategy for the control of epidemics and pandemics.


The purpose of this survey was to estimate the prevalence of viral load (VL) suppression and emergence of HIV drug resistance (HIVDR) among individuals receiving antiretroviral therapy (ART) for 36 months or longer in Viet Nam using a nationally representative sampling method.The survey was conducted between May and August 2014 using a two-stage cluster design. Sixteen ART clinics were selected using probability proportional to proxy size sampling, and patients receiving ART for at least 36 months were consecutively enrolled. Epidemiological information and blood specimens were collected for HIV-1 VL and HIVDR testing; HIVDR was defined by the Stanford University HIVDR algorithm.Overall, 365 eligible individuals were recruited with a mean age of 38.2 years; 68.4% were men. The mean time on ART was 75.5 months , and 93.7% of the patients were receiving non-nucleoside reverse transcriptase inhibitor-based regimens. Of the 365 individuals, 345  had VL below 1000 copies/mL and 19  had HIVDR mutations.Our nationally representative survey found a high level of VL suppression and a low prevalence of HIVDR among individuals who received ART for at least 36 months in Viet Nam. Continued surveillance for HIVDR is important for evaluating and improving HIV programs. There were an estimated 250 000 people living with HIV in Viet Nam in 2016.  was first introduced in Viet Nam in the mid-1990s and has been rapidly scaled up since 2005, with a total of 115 927 people receiving ART at the end of 2016.  recommended the prospective cohort studies of patients in conveniently selected sentinel sites to assess the emergence of ADR. ; , which tested specimens from eight outpatient clinics in the north of the country, and the Pasteur Institute in Ho Chi Minh City (PI HCMC), which tested specimens from eight outpatient clinics in the south of the country.HIV-1 RNA viral quantification was conducted using the automated Abbott real-time HIV-1 assay (in NIHE) and the automated Roche Cobas AmpliPrep/Cobas TaqMan HIV-1 assay (in PI HCMC) with detection limits of 20 copies/mL. HIVDR genotypic test was conducted on the pol gene with the ABI 3130XL system using the Big-Dye Terminator v3.1 Cycle Sequencing Kit . HIVDR was interpreted using the HIValg Program on the Stanford University HIV Drug Resistance Database web site.  and statistical analysis was performed using STATA version 11 . Standard descriptive statistics were calculated for categorical and continuous variables. Data analysis for prevalence of VL suppression was conducted in STATA using the survey (svy) suite of commands. Data were weighted by clinic size . .During the enrolment period, a total of 6920 patients were screened at 16 sampled clinics, from which 368 eligible patients were recruited. Three patients were excluded because the duration of ART was less than 36 months; therefore, 365 persons were included in the final analysis. Their baseline characteristics are summarized in Among the 365 participants with VL testing, 345 (95.1%) achieved VL suppression (defined as VL < 1000 copies/mL). The prevalence of VL suppression among individuals on first-line ART was 94.8% (95% CI: 92.1–96.6%)  and among persons with VL > 1000 copies/ml was 94.7% (95% CI: 64.1–99.4%) , K103N , V106I  and G190A . For NRTI resistance, the most common resistance mutations were M184V ; V75M ; and thymidine analogue mutations (TAMs), consisting of T215F/I/Y , K219E/Q , K70R , D67N , M41L  and L210W . There were 45% (9/20) of patients harbouring viruses with three or more TAMs Of the 20 patients failing ART, 75% (15) had mutations that predict resistance to tenofovir, while 85% (17) and 70% (14) had mutations that predict resistance to either lamivudine (3TC) or emtricitabine (FTC) and ZDV, respectively (3 and aOR = 11.4 (95% CI: 2.51–51.9) for CD4 between 100 and 350 cells/µl.This study was the first survey of ADR in Viet Nam following the WHO guidance for ADR surveillance released in 2014, , pre-treatment HIV drug resistance (PDR) and ADR. In a study among 70 newly diagnosed HIV-positive clients aged 18–24 years in Hanoi in 2006, the prevalence of TDR was at a low level , . This study result is also in line with other studies showing that Viet Nam’s programmes are achieving a high level of VL suppression (< 1000 copies/ml) at 12 months as reported from cohort studies (Our study found a strong association between VL and drug-resistance mutations among patients receiving ART for at least 36 months. HIVDR mutations to NNRTI were detected in 19 out of 20 (95%) patients with VL > 1000 copies/ml supporting the notion that a prompt switch to second line therapies is needed in people with detectable virus despite treatment. Because HIVDR is associated with a low level of adherence (more importantly with NNRTI resistance emergence)  were 11.5% (95% CI: 7.8–15.1) at 10–14 months and 10.3% (95% CI: 6.9–13.8) at 22–24 months. The percentages of patients with detectable VL that had drug-resistance mutations were 75.9% at 10–14 months and 86.2% at 22–24 months. , V75M and M184V (NRTI resistance). (The mutation pattern in our study was similar to the results of an ADR survey at three clinics in Ho Chi Minh City in 2009–2010. Among the 22 patients with HIVDR mutations at 12 months, resistance to NRTIs, NNRTIs and to both classes were reported as 4.5%, 9% and 90%, respectively. At 24 months following ART initiation, there were 25 cases with HIVDR mutations: 96% were resistant to both NRTIs and NNRTIs, 0% were resistant to NRTIs alone and 4% were resistant to NNRTIs alone. (In conclusion, this is the first survey to describe nationally representative levels of VL suppression and ADR in adults receiving ART for at least 36 months in Viet Nam. The survey found high levels of VL suppression, low levels of ADR among people on ART and high levels of HIVDR among people failing ART, suggesting that Viet Nam had successfully managed its programme quality to maintain ADR at a low level at the time the survey was conducted in 2014.


The finding of high COHgB levels in young children in rural Malawi that are at levels at which adverse neurodevelopmental and cognitive effects occur is of concern. Effective approaches for reducing exposure to CO and other constituents of air pollution in rural sub-Saharan African settings are urgently needed.Household air pollution is estimated to cause half a million deaths from pneumonia in children worldwide. The Cooking and Pneumonia Study (CAPS) was conducted to determine whether the use of cleaner-burning biomass-fueled cookstoves would reduce household air pollution and thereby the incidence of pneumonia in young children in rural Malawi. Here we report a cross-sectional assessment of carbon monoxide (CO) exposure and carboxyhemoglobin (COHgB) levels at recruitment to CAPS. Mean  48-h CO exposure of 1928 participating children was 0.90  ppm and mean  COHgB level was 5.8% . Higher mean CO and COHgB levels were associated with location (Chikhwawa versus Chilumba) (OR 3.55 (1.73–7.26)); (OR 2.77 (1.08–7.08)). Correlation between mean CO and COHgB was poor (Spearman’s ρ = 0.09,  Seven-hundred-million people in Africa use biomass fuels to provide energy for cooking, heating and lighting . Women aIn Malawi, there is almost universal use of biomass fuels for cooking ,6, air qCleaner burning biomass-fueled cookstoves are now available that have improved combustion efficiency compared to traditional open fires. Some of the more advanced technologies  have emissions that are up to 90% and 75% lower in laboratory and field tests, respectively, than open fires . There iAlthough particulate matter (PM) 2.5 is considered the “gold standard” for household air pollution monitoring, it has historically been difficult to accomplish personal PM 2.5 measurements, particularly in children. CO is easier to measure and has been used as a marker for exposure to products of combustion as it is a major component of household air pollution , and hasIn the setting of a cookstove intervention trial in rural Malawi, we obtained baseline  personal CO exposure and COHgB levels in a large sample of children participating in the trial. We report these data to provide evidence of the background exposure to household air pollution of children living in rural sub-Saharan Africa.n = 150 communities; n = 8626 households; n = 10,750 children) to determine whether a cleaner burning biomass-fueled cookstove intervention would reduce pneumonia incidence in children under 5 years of age living in rural Malawi [We conducted the Cooking And Pneumonia Study (CAPS), a community-level cluster randomized controlled trial  near the Shire River in the South, that experienced droughts, flooding and famine during the time of the study; and Chilumba, an agrarian and fishing community on the shore of Lake Malawi that is 75 km from the nearest urban center  in the North. Recruitment of participating households in both Chikhwawa and Chilumba primarily occurred during the dry season, but ongoing enrollment also occurred if new participants came within the catchment area during the trial period. Homes in these rural areas tended to be made of brick and to consist of one main room divided by partitions. Cooking occurred within the home, which may have had limited ventilation, or be outdoors, often under a veranda or in an area that may have partial walls or roof. The rainy season in this region generally encompasses November through February with a slightly cooler season from June through August. The remainder of the year is routinely hot and dry.All children with parental or guardian consent aged up to 4.5 years living in the clusters across the two districts were eligible to participate in CAPS. Recruitment took place between 9 December 2013 and 28 February 2016. All participants in CAPS, including those that became eligible during the course of the trial, through movement into the catchment area or as new births, were also potentially eligible for this sub-study. The primary CAPS outcome was WHO Integrated Management of Childhood Illness (IMCI)-defined pneumonia in children under the age of 5 and children were enrolled up to the age of 4.5 in order to obtain at least 6 months of data on each participant. At the time of enrollment, a random number was created and children who fell within a predetermined range were recruited with the aim of randomly selecting 1 in 4 CAPS participants for this sub-study. The larger CAPS sample size was designed to have 90% power to detect a 20% difference in the pneumonia incidence rate between the intervention and control groups assuming a baseline rate of 5 per 100 child years. The 25% sample size for the CO monitoring sub-study was based on feasibility, given the large number of children recruited for CAPS.Fieldworkers trained using study-specific standardized operating procedures attended the homes of all participating children to interview their parents or guardians at the baseline data collection visit for CAPS. Electronic Case Report Forms (CRFs) programmed using Open Data Kit (ODK) software and administered offline using smart phones  were used to collect data. Demographic, socioeconomic, health and household air pollution data were collected in this way at enrolment. An additional questionnaire was administered at the time of collection of CO monitors (see below) about potential smoke exposures and cooking practices including number of meals, fuel used and cooking location during the time CO monitors were worn.Portable CO monitors  were used to conduct continuous, real-time monitoring of exposure to CO. Measurements were taken at 30-s intervals. Lascar monitors have a measurement range of 0–1000 ppm with an accuracy of ±6% of the reading. They can operate within a temperature range of −10 to +40 °C. Field workers utilized laptop computers to initiate the monitors at the time of arrival at the participants’ homes. The participants were instructed to wear the monitors at all times during the 48-h monitoring period, with the exception of when the child was sleeping, there was rain, or if the monitor could be exposed to water through swimming or bathing. Monitors were removed at night and put beside the child. The Lascar monitors were placed in a cloth bag made of local fabrics that was attached around the child’s neck with Velcro . CO dataAt the time of monitor collection, transcutaneous COHgB measurements were obtained on the children . These monitors have a range of 1–40% and can operate within a temperature of −18 to +54 °C. Prior to use each day, the COHgB monitor was checked with the manufacturer’s testing device (Masimo Rainbow Tester) to confirm accuracy. Three COHgB measurements were obtained on each child, ideally on three separate digits. As the precision of the monitor is ±3%, if the range of results was greater than 6% then an additional measurement was obtained. A pediatric sensor that was suitable for children aged 6 months and above was used; children younger than 6 months were not eligible for COHgB measurements and, therefore, only had CO monitoring.Data were transferred from the smart phones and laptops to a secure server at each study site and then onwards to a central secure server at the Liverpool School of Tropical Medicine for cleaning and preparation for analysis.p < 0.05 were included in a multivariable analysis. T tests were used to compare group means. Log means were utilized for analyses requiring a normal distribution. Chi-squared tests were used for categorical outcomes. Spearman’s rho was used for correlation between nonparametric values. A p value < 0.05 was considered statistically significant.R software was used to process the CO monitor measurements. CO monitor files with greater than 5790 measurements were truncated at the first 48 h of assessment. Files containing fewer than 500 measurements were excluded. Files with incorrect data and non-plausible graphs were removed for quality control. STATA (Version 14) was utilized for the overall analysis including the questionnaire results. Multilevel logistic regression modeling, taking cluster and household effects into account, was used to identify factors associated with higher CO and COHgB. Higher CO and higher COHgB were defined as greater than the median for the sample. The regression analysis was a two-step process: Univariable analysis was performed initially and variables having associations with The Lancet [The study was approved by the College of Medicine Research Ethics Committee in Malawi (Ref P.11/12/1308) and the Liverpool School of Tropical Medicine Research Ethics Committee in the UK (Ref 12.40). Parents and guardians of children gave written informed consent prior to participation. When needed, information sheets and consent forms were read out with witnessed thumbprints taken in place of signatures. The CAPS protocol was peer reviewed and published by e Lancet . Trial rThe funders had no role in the study design, data collection, analysis, interpretation or writing of the report. The corresponding author had full access to all the study data and had final responsibility for the decision to submit for publication.A total of 10,750 participants  from 8550 households and 150 communities were recruited into CAPS. Mean age (SD) was 24 (16) months and 50.7% were female .We randomly selected 2294 of these participants for inclusion into this sub-study and 1994 consented to participate. Of these, 1928 children  from 1870 households contributed data for analysis; 24 participants did not enter the study in the end and 42 participants had insufficient or unusable data. COHgB measurements were obtained from 1520 children . The meap < 0.001).There were socioeconomic and lifestyle differences in participants from Chilumba and Chikhwawa with more indicators of poverty in Chikhwawa: Open defecation was used for toileting by 31% of the Chikhwawa participants while 93% of children from Chilumba had at least simple pit latrines; 6.9% of Chikhwawa participants reported a formal water access point, while 75% in Chilumba had access to a bore hole for water and 36% to a household or communal tap. A significantly larger percentage of Chikhwawa participants were in households where they lacked sufficient food (77% vs. 36%) and money for soap (83% vs. 52%) compared to Chilumba .At baseline, 61% of parents/guardians reported that their child had experienced a cough in the 3 months prior to enrollment, 17% had wheezing, 6% a burn and 16% a diagnosis of pneumonia in the 12 months prior to enrollment. Fewer wheezing symptoms (14%) were reported in Chikhwawa compared to Chilumba (20%)  reported cooking outside in a separate walled and roofed structure in the dry season, 14% reported cooking outside in a structure with a roof only, 20% cooked on a veranda, and 23% reported cooking in the open air. In the rainy season, less than 1% would attempt to cook outside in the open air, 12% outside under a roof, 44% outside in a structure with roof and walls, with 23% reporting cooking on a veranda, 9% inside in a kitchen and 14% inside in a living area.Survey data on 10,821 individual cooking events were collected. Respondents reported a mean (SD) of 5.54 (1.11) cooking events during the 48-h monitoring period. Although the majority of households did not receive the Philips stove until the monitoring had been completed, a small number did have them due to the logistics of stove delivery in this rural area and the ongoing enrollment process. If a Philips stove was available, the participating household was asked not to use it until the monitoring period was completed, although a small number (1%) of the cooking events were reported as utilizing the Philips stove. Most individual cooking events were reported as lasting 1–2 h (46%), with 39% lasting 30–60 min, 10% lasting 2–3 h, fewer than 5% lasting less than 30 min, and fewer than 1% lasting over 3 h.The majority of households cooked most often with wood (58%) over an open fire (60%); 17% reported using charcoal, with 5% indicating they owned a charcoal burner. Less than 1% of households reported the use of any other type of improved cookstove at baseline.Many participants reported extraneous exposures to smoke not from cooking, with the majority noting another source of exposure, including burning rubbish in the village (42%), cooking for others as a business (14%), brick making (5%), and use of kerosene lamps (2%). The presence of one or more smokers was noted in 17% of the households.CO exposure monitoring data were obtained from 1928 participants . The median CO was 0.50 ppm with an interquartile range (IQR) of 0.21–1.00. Only 20 participants had average CO levels above the WHO recommended 24-h CO exposure level of 7 ppm; however, 51 participants experienced CO exposures over the recommended 15-min exposure level of 100 ppm and 120 participants had 1-h averages greater than the recommended level of 10 ppm .Self-reported burns (OR 1.63 (1.02–2.60)), living in Chikhwawa vs. Chilumba (OR 7.11 (4.11–12.28)), and cooking inside in the dry (OR 2.13 (1.36–3.36)) and rainy seasons (OR 1.38 (1.07–1.78)) were associated with CO levels higher than the median. Poverty as evidenced by lack of food (OR 1.91 (1.44–2.54)) and lack of money to buy soap (OR 1.57 (1.20–2.06)) were also associated with higher CO levels. In multivariable analysis, occurrence of burn (OR 1.58 (1.00–2.50)), lack of food (1.50 (1.14–1.95)), and living in Chikhwawa vs. Chilumba (OR 3.55 (1.73–7.26)) were independently associated with having a higher CO level after controlling for other factors .Mean (SD) CO exposure was higher in Chikhwawa (1.27 ppm (2.79)) versus Chilumba participants (0.62 ppm (1.79)). Higher mean (SD) CO levels were noted as households cooked in more confined areas during the dry season: outside under a roof only, 0.56 ppm (0.92); veranda, 1.39 (2.97); inside in kitchen, 1.83 ppm (3.64); and inside in a living room 1.75 ppm (2.52). Similar levels were noted for the cooking areas during the rainy season: outside under a roof only, 0.52 ppm (0.90); veranda, 1.19 (2.74); inside in kitchen, 1.65 ppm (5.71); and inside in a living room, 1.16 ppm (1.52).To assess exposures during potential cooking events, maximum 15-min, 30-min, 60-min and 90-min exposures were determined for each participant. Mean (SD) CO levels were 23.0 ppm/minute (29.0) for the maximum 15-min intervals, 16.8 ppm/minute (23.8) for maximum 30-min exposures, 12.0 ppm/minute (19.0) for maximum 60-min exposures, and 8.6 ppm/minute (13.4) for the maximum 90-min exposures.p < 0.001), in Chikhwawa versus Chilumba participants, (6.9% (3.7) vs. 5.0% (2.8), p < 0.001) and in males versus females (6.0% (3.3) vs. 5.6 (3.3), p = 0.03).COHgB was measured in 1520 participants; 215 were under 6 months of age and 193 had incomplete data. Mean (SD) COHgB was 5.8% (3.3) and ranged from 0 to a maximum of 20.3%. Median COHgB was 5.5% (IQR = 3.3–7.7). A 4th COHgB measurement was taken in 531 (35%) of the children. There was a statistically significant difference in the mean (SD) COHgB between children who required three vs. four measurements (5.4% (3.5) vs. 6.6% (2.9), Male gender (OR 1.35 (1.02–1.79)), living in Chikhwawa (OR 3.12 (1.89–5.14)) and poverty as evidenced by lack of food (OR 1.47 (1.09–1.99)) were associated with COHgB levels higher than the median. In multivariable analysis, only living in Chikhwawa independently remained a positive association (OR 2.77 (1.08–7.08)) .p < 0.001), CO total exposure  and the mean CO level obtained during the final 6 h of monitoring, just prior to COHgB testing . Mean COHgB levels did not correlate with overall maximum CO exposures , maximum 15-min , maximum 30-min , maximum 60-min  or maximum 90-min exposures .Mean COHgB levels were weakly positively associated with mean CO levels  and at levels commonly associated with tobacco smoke exposure. Personal exposure to CO correlated poorly with COHgB levels.The mean 48-h CO exposure of 0.90 ppm that we report for rural Malawian children is consistent with levels noted in other household air pollution research from developing countries: 0.93–2.19 ppm in Guatemala ; 1.04 pptemazcal [Other studies of COHgB associated with biomass fuel use have shown results from 15.74% in India due to cooking events  and 12–1temazcal . At a sttemazcal . Howevertemazcal , would rtemazcal .There have been few studies in Africa that have compared CO and COHgB levels within the context of household air pollution and none that have used non-invasive COHgB testing. In this study, there was not a good correlation between these two measures, a finding that has been seen in other long-term environmental CO monitoring studies . Lam et Based on the results of the questionnaires, CO levels and COHgB in this study represent not only exposures from biomass cooking, but also potentially from many other sources of combustion, although the self-reported exposures to these other sources were not significantly associated with either personal CO or COHgB in the multivariable analysis. Exposure to burning rubbish was frequently reported as was exposure to burning of agrarian waste, which may be burned for extensive periods of time to prepare fields for future crops and to remove small pests. Chikhwawa is also situated next to Malawi’s largest sugar cane grower, whose large fields were routinely burned prior to harvesting. Making beer at home is also a lengthy process requiring 6–8 h of boiling for distillation of each pot. Personal CO exposures from cooking also reflect the frequent occurrence of outdoor and open-air meal preparation, particularly in the dry season, which is the majority of the year in Malawi. Exposure assessment that could clarify the contributions of various combustion sources would assist in avoiding future interventions that may have limited value in decreasing household air pollution.This sub-study of CAPS has several strengths. It is the largest study to date to measure personal exposure to markers of biomass smoke of young children in Africa. The use of real-time monitors for CO provided the ability to obtain detailed exposures, including the assessment of peak levels, over a 48-h monitoring period. The non-invasive measurement of COHgB added a specifically relevant biomarker of exposure. Limitations include likely information bias in terms of questionnaire ascertainment of cooking behavior. There was a low number of positive responses in the Chikhwawa population with respect to household characteristics such as type of fuel used, suggesting problems with the questionnaire or its administration by fieldworkers as many households did not report cooking with any fuels or having any toileting options. Although all field workers were trained and all surveys piloted extensively, Chikhwawa was the first site of enrollment and data acquisition improved over time. The lack of a neonatal sensor also led to the exclusion of COHgB readings on the youngest children.The use of biomass fuels is ubiquitous in rural Malawi. Exposures to smoke occurs commonly from a range of sources including household cooking, commercial cooking, rubbish and agricultural field burning. The COHgB levels we observed in young children in rural Chikhwawa were high and at levels associated with adverse health outcomes including adverse neurobehavioral and cognitive effects. We conclude that children in settings like those in rural Malawi in Africa are likely exposed to potentially harmful levels of CO from multiple sources and intervention with cleaner-burning biomass-fueled cookstoves alone may not be sufficient to deliver improved health outcomes. More comprehensive environmental interventions that substantially improve air quality are likely needed to achieve this.


Wheat bran (WB) and rice bran (RB) are the agricultural by-products used as poultry feed in many developing countries. However, their use for poultry feed is limited due to high fiber and the presence of anti-nutritional substances (e.g. β-glucans). The objective of this study was to develop a method to improve the quality of those brans by reducing the fiber content.A two-step fermentation method was developed where the second fermentation of first fermented dry bran was carried out. Fermentation was performed at a controlled environment for 3 h and 6 h (n = 6). The composition of brans, buffer solution and rumen liquor was maintained in a ratio of 1:2:3, respectively. Brans were analyzed for dry matter, crude fiber (CF), acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) content. Celluloses and hemicelluloses were calculated from the difference of ADF-ADL and NDF-ADF, respectively. Samples were compared by two-factor analysis of variance followed by Tukey’s multiple comparison tests (p<0.05).CF %, ADF % and cellulose tended to decrease and NDF % and hemicellulose content was reduced significantly (p<0.05). After the 1st fermentation step, NDF decreased 10.7%± 0.55% after 3 h vs 17.0%±0.78% after 6 h in case of WB. Whereas, these values were 2.3%± 0.30% (3 h) and 7.5%±0.69% (6 h) in case of RB. However, after the 2nd fermentation step, the decrease in the NDF content amounted to 9.1%±0.72% (3 h), 17.4%±1.13% (6 h) and 9.3%±0.46% (3 h), 10.0%±0.68% (6 h) in WB and RB, respectively. Cellulose and hemicellulose content was reduced up to 15.6%±0.85% (WB), 15.8%±2.20% (RB) and 36.6%±2.42% (WB), 15.9%±3.53% (RB), respectively after 2nd fermentation of 6 h.Two-step fermentation process improved the quality of the brans for their use in poultry feed. In developing countries, poultry industry plays a vital role to meet the protein requirements for human nutrition. For this reason, the production efficiency of poultry to convert feed into meat is essential ,2. The pFirst some experiments (single-step fermentation) were performed to find optimum fermentation conditions. Fermentation of WB was carried out in different composed mixtures under different environmental conditions. Fermentation was started with 48 h then reduced gradually  to 3 h due to the instability of the pH for fiber degrading bacteria. It was found that, optimum pH (6.5) was maintained up to 3 h then decreased to 5.1, 4.7, 4.2, 4.0, 3.9, and 3.8 after 6, 8, 12, 24, 36, and 48 hours, respectively. As optimum pH could not be maintained, further nutrient analyses were not performed. But few samples (from 3 h fermentation) when analyzed it was observed that, after 3 h the fiber content did not decrease satisfactorily then a method with two-step fermentation was performed.It would be feasible to implement the developed method at the industrial scale production. The new fermentation technology will be of great benefit and would probably be provided to the rice/wheat miller for the production of fermented brans in a large scale to introduce a quality product to the feed market for economic poultry ration formulation. It might also be possible to use this technology to ferment other kinds of fibrous feed for feeding other non-ruminants. A designed fermenter would be useful for industrial scale production and a fermenter might be designed to facilitate all the conditions suitable for proper growth of rumen microorganisms. In developing countries, it is promising to collect rumen liquor as well as rumen content from different slaughtered cows and goats that would be a good sources of inoculum. It might be useful to reuse the effluent after fermentation as a inoculum source for large scale production. It might also be feasible to grow rumen bacteria in a laboratory that might be useable for large scale fermented feed production.Considering all these factors, the present study (two-step fermentation) was undertaken to systematically evaluate and compare the effect of fermentation on nutritional improvement of WB and RB after fermentation with rumen liquor. The hypothesis is that, fermentation of different kinds of brans with rumen liquor will improve their nutritive value by decreasing the fiber content. Thus the aim of this present study was to develop an easy and cheap method for optimum fermentation of high fiber feedstuffs to improve their nutritive value due to lower fiber content.All experiments  were performed at the laboratory of the Institute of Animal Nutrition, Vetsuisse faculty, University of Zurich, Switzerland according to the animal welfare law of Switzerland.McDougall buffer  solution2 gas before collection to maintain the anaerobic condition. After collection, the rumen liquor was filtered through a stainless steel round strainer to remove any solid particle present. After that the filtered rumen liquor was transferred immediately (within 5 minutes) into fermentation containers with constant flushing of CO2 gas to maintain anaerobic condition [Rumen liquor was obtained from a cannulated Brown Swiss cow, Department for Farm Animals, Vetsuisse Faculty, University of Zurich, Switzerland that was fed with hay and concentrates for maintenance. Rumen liquor collection was done before morning feeding  in a preondition . Simultaondition . Only ifDue to the results of pH and crude nutrient measurements after the single-step fermentation, a two-step fermentation was tested. In the two-step fermentation, a 2nd fermentation of the 1st fermented bran after drying of the mixture from the first fermentation (1st step) was performed . For the3  that was added after 3 h to maintain an appropriate pH for the entire period of fermentation. This was done because after 3 h, the pH started to decrease below 6 in the 3 h fermentation process. Due to this fact, at that point an additional buffer substance was added to increase the pH near to 7. The same procedure was used in both steps of fermentation. Composition mixture of fermentation and experimental procedure was similar in the 6 h like in the 3 h fermentation. The pH was measured as described in the pH measurement schedule  of this two-step fermentation process for nutrient analysis: stage 1, fresh bran; stage 2, bran before 1st fermentation step, after addition of rumen liquor with buffer and bran mixture; stage 3, 1st fermented dried bran after 1st fermentation step; stage 4, fermentation product mixture (1st fermented product+buffer+rumen liquor), before 2nd fermentation step; and stage 5, 2nd fermented dried bran after 2nd fermentation step.All samples were analyzed for crude fiber  and van Soest fibers  accordinThe experimental procedure was similar as in WB fermentation. The only difference was in the 6 h fermentation. During the 2nd step of the 6 h fermentation, no additional buffer substances were added as pH was stable enough (pH: 7) in that case.Statistical analyses were performed using the statistical software package IBM SPSS, version 23 . All nutrients data were analyzed by two-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison tests (p<0.05). Factors in the analyses were time and different stages of fermentation. The pH measurement data were analyzed by one-way ANOVA for 3 h and 6 h separately, as pH measurement stages were different between 3 h and 6 h fermentation. The results are given as mean±standard error of mean.During 3 h fermentation, a pH of 6 to 6.5 was maintained in both fermentation steps and the pH was decreased significantly (p<0.05) from the initial value of 7 in both of the steps .The 6 h fermentation was started at pH 7 (7.0±0.03) like the 3 h fermentation. The pH was significantly (p<0.05) reduced after fermentation . After tThe NDF content of WB was significantly (p<0.05) reduced after fermentation during the 1st (stage 2 to 3) and 2nd (stage 4 to 5) step of fermentation for both 3 h (p = 0.000 for both 1st and 2nd step) and 6 h (p = 0.000 for both 1st and 2nd step) fermentation . In thatAnalyses of the CF and ADF content data showed no significant (p<0.05) reduction of these nutrients after fermentation except in the 2nd step of the 6 h fermentation, where CF was significantly reduced (p = 0.001) . Only a Data are presented in The pH measurement data of 3 h fermentation is presented in 3 up to the end of the process after 6 h.Data are presented in The effects of fermentation on NDF content are presented in Data of CF, ADF, and ADL content are presented in Percentages of cellulose and hemicellulose reduction of fresh bran as well as fermented RB are presented in In the present investigation, a two-step fermentation was performed after examining the results of some initial experiments (single-step fermentation) of this study that were conducted based on some in-vitro studies ,23 such 3 were added to increase the pH and with this the fermentation capability of the system. As an appropriate pH (6 to 6.5) could maintained only during the 3 h fermentation, the additional buffer substance was added to control pH during the 6 h fermentation. NaHCO3 is a potent buffer to prevent acidic conditions in the rumen or used in in-vitro system [The pH is one of the most important factors for ruminal microorganisms for the fermentation of fibrous components ,25. The o system ,24 and wBacillus subtilis, Aspergillus niger and A. oryzae for 7 days significantly (p<0.05) reduced CF . Also during fermentation of RB with Bacillus amyloliquefaciens and humic substances for 3 and 5 days, CF was reduced from 10.62% to 8.37% and 8.36%, respectively [In the described fermentation system, fiber was degraded more during 6 h compared to 3 h fermentation and the fiber was decreased more in the 2nd fermentation step. This could be expected as fermentation time is a very important factor for fiber degradation and Wizna et al  reportedectively ,36. Alsoectively , the NDFectively ,40. Addiectively  and therectively . Howeverectively .in-vitro fermentation of feed stuffs to improve their nutritive value seems to be an easy but effective method. However, in future, the method should be optimized by a longer fermentation time and the improvement of nutritive value, especially the changes of amino acid (AA) profile should be investigated. Additionally, the method should be adapted for an industrial use for the improvement of the nutritional quality of different kinds of fibrous feed to be able to produce meat at affordable price. This would help to provide humans in the third world with AAs in a sufficient amount.The results obtained in this study suggest that fermentation of WB and RB with rumen liquor improved their nutritional value by decreasing the fiber content. Fiber degradation was strongly influenced by the time of fermentation and a 2nd fermentation step had a significant effect on fiber reduction compared to the 1st fermentation step. Further the procedure of fermentation should be adjusted according to the fermented feed-stuffs. In all


The Multi-angle Imaging SpectroRadiometer (MISR) aboard NASA’s Terra satellite is one of few satellite aerosol sensors providing information of aerosol shape, size and extinction globally for a long and continuous period that can be used to estimate PM2.5 speciation concentrations since year 2000. Currently, MISR only provides a 17.6 km product for its entire mission with global coverage every 9 days, a bit too coarse for air pollution health effects research and to capture local spatial variability of PM2.5 speciation. In this study, generalized additive models (GAMs) were developed using MISR prototype 4.4 km-resolution aerosol data with meteorological variables and geographical indicators, to predict ground-level concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) in Southern California between 2001 and 2015 at the daily level. The GAMs are able to explain 66%, 62%, 55% and 58% of the daily variability in PM2.5 sulfate, nitrate, OC and EC concentrations during the whole study period, respectively. Predicted concentrations capture large regional patterns as well as fine gradients of the four PM2.5 species in urban areas of Los Angeles and other counties, as well as in the Central Valley. This study is the first attempt to use MISR prototype 4.4 km-resolution AOD  components data to predict PM2.5 sulfate, nitrate, OC and EC concentrations at the sub-regional scale. In spite of its low temporal sampling frequency, our analysis suggests that the MISR 4.4 km fractional AODs provide a promising way to capture the spatial hotspots and long-term temporal trends of PM2.5 speciation, understand the effectiveness of air quality controls, and allow our estimated PM2.5 speciation data to be linked with common spatial units such as census tract or zip code in epidemiological studies. This modeling strategy needs to be validated in other regions when more MISR 4.4 km data becoming available in the future.Research efforts to better characterize the differential toxicity of PM  For exa009–2011 . Walker 009–2011 . Another000–2006 . Anotherectively .2.5 mass concentrations with wide spatial and temporal coverages  aboard NASA’s Terra spacecraft provides information of aerosol shape, size and extinction globally. Detailed descriptions of the MISR retrieval algorithm and MISR data structure are given elsewhere , which is expected to be available in mid-2018. A prototype version of this product has demonstrated improved quality in retrieved total AOD in comparison with measurements from the Aerosol Robotic Network (AERONET) . Further research is needed to evaluate whether the upcoming globally available MISR 4.4 km operational data can enable worldwide characterization of PM2.5 hotspots and detect the spatial gradients of PM2.5 constituents within urban areas at a higher spatial resolution.Another approach to predict ground-level particulate pollution is based on remotely sensed aerosol products. Although statistical models driven by remotely sensed total column aerosol optical depth (AOD) can effectively predict ground-level total PMoverages , most salsewhere . Brieflyeciation , 2007b. eciation . This reAERONET) . Moreovelifornia . They di2.5 chemical components, including sulfate, nitrate, OC and EC in Southern California, and explore the long-term temporal and spatial trends of these PM2.5 components based on model predictions. This case study illustrates a novel capability for predicting concentrations and capturing the high spatial variability of PM2.5 species using satellite remote sensing data at relatively high spatial resolution and provide references for future applications, e.g. epidemiological studies that focus on PM2.5 differential toxicity requiring exposure estimates of a high spatial resolution, spatial analysis to detect hotspots of certain PM2.5 constituents, and long-term trends analysis of PM2.5 speciation for assessing emission control policies. Once the Version 23 MISR aerosol product is publicly released and the global data record reprocessed to 4.4-km resolution, the methodology can be extended to other regions of the world.The objectives of this study are to develop statistical models with prototype MISR 4.4-km global mode aerosol microphysical properties to predict ground-level concentrations of major PM2.2.1.Our study domain is set in Southern California, including the counties of Kern, Santa Barbara, Ventura, Los Angeles, Orange, and part of the counties of Fresno, Kings, Tulare, Inyo, San Luis Obispo, San Bernardino, Riverside and San Diego. This area is covered by the MISR aerosol data of blocks 62–64 in path 41 .2.2.2.2.1.This analysis used prototype of the MISR Level 2 (L2) aerosol data for the years 2001–2015, which was provided by the Jet Propulsion Laboratory (JPL). Differences between this prototype product and the data that will be delivered as the new operational (V23) aerosol product are minor and will not affect the conclusions of this work. The spatial resolution of the aerosol product is 4.4 km × 4.4 km. According to the latest validation results, the correlation coefficient between MISR4.4 km AOD and AERONET AOD is 0.96 based on data of AERONET-DRAGON  sites in San Joaquin Valley, Washington DC-Baltimore, Seoul of Korea, and Osaka of Japan . This aemixture j is the AOD of mixture j; Fractioncomponent i in mixture j is the contribution of component i to the total AOD for mixture j; if mixture j is retrieved successfully, then α = 1, otherwise α = 0.The fractional AOD values can be calculated for any given MISR aerosol observation from 2.2.2.2.5 mass and speciation concentrations from the U.S. Environmental Protection Agency’s (EPA) urban Chemical Speciation Network (CSN) (http://aqsdr1.epa.gov) and Federal Land Manager Environmental Database (http://views.cira.colostate.edu/fed), respectively. The locations of PM2.5 speciation sites in our study domain are displayed in 42−), nitrate (NO3−), organic carbon (OC) and elemental carbon (EC) were selected for model development, since they were the major PM2.5 components in the study domain   and remork (CSN)  were obty domain . The samy domain ; and they domain , 2012. Tlsewhere  and werelsewhere  OC_a2.5 OC and EC concentrations from collocated sites of CSN and IMPROVE in 2005 and 2006. A represented the monthly positive organic artifact resulting from filter adsorption of semivolatile organic compound (SVOC), and M indicated the multiplicative negative organic carbon artifact related with volatilization of collected OC mass  collection 6 MOD13A2 (https://modis.gsfc.nasa.gov/data/dataprod/mod13.php) normalized difference vegetation index (NDVI) were used in this study to indicate the changes of land surface cover. This NDVI product is provided every 16 days at 1-km spatial resolution as a gridded Level-3 product in the Sinusoidal projection. Digital maps of major roads (including highways) for year 2011 were obtained from ESRI StreetMap USA  to indicate the local traffic emission. Elevation data were extracted from the prototype 4.4 km MISR aerosol dataset that were based on the Digital Elevation Model (DEM) (https://asterweb.jpl.nasa.gov/gdem.asp) used by the MISR project, which indicates the geographic topography. Elevation data remain constant for the entire study periods.Previous research showed that elevation and seasonal variability of PBLH might contribute significantly to the differences of PMn winter . Daily mn winter . Additio2.2.4.2.5 speciation and MISR fractional AODs. Day of the year (DOY) and the year index (Year) were included as covariates to account for systematic seasonal and long-term trends.The geographic coordinates of CSN and IMPROVE sites  were included as spatial indicators to reflect the potential impact of site locations on the association between PM2.3.A 2.5 km-radius buffer was made for each CSN and IMPROVE site so that the buffer area is approximately equal to the area of a MISR 4.4 km pixel. MISR fractional AODs were assigned to each CSN and IMPROVE site if the MISR pixel centroid falls within the buffer zone. The average NDVI of all matched pixels in the buffer zone was assigned to the site. Inverse-distance weighting was used to interpolate the PBLH values to the locations of CSN and IMPROVE sites. Lengths of major roads within the buffer zone were calculated for each CSN and IMPROVE site. All covariates of the prediction dataset were integrated based on the locations of the MISR 4.4 km grid cells.2.4.2.5 component with MISR fractional AODs as primary predictors and other variables, including PBLH, land use predictors, temporal indicators and spatial indicators mentioned above. A GAM allows the predictor variables to have nonlinear relationships with the dependent variable by using semi-parametric spline smoothers. The model structure for sulfate can be expressed in fi  is smooth term of MISR fractional AODi; fx,y  is a 2-D smooth spatial function of site locations; f (DOY) and f (Year) are a nonlinear term of DOY and the year index varying smoothly in time, respectively; and f (elevation), f (NDVI), f (road length) and f (PBLH)are smooth terms of elevation, NDVI, road length and PBLH, respectively. Cubic regression splines are used for all smooth terms except fx,y .Generalized additive models (GAM) were developed for each PM2) was used as the selection criterion and as a primary indicator of model performance  (2.5 species) were calculated to quantify model prediction errors.Predictor variables statistically significant at the α = 0.10 level were kept in the final model. Since MISR data are sparse and the data size is small in this analysis, adjusted coefficient of determination (Rformance . Leave-olopment) . If the 3.3.1.2.5 species with MISR fractional AODs and other predictor variables, we obtained 622 site-days of data for the years 2001–2015 for sulfate and nitrate, and 624 site-days of data for the years 2001–2015 for OC and EC in the model fitting datasets. All successfully retrieved AOD values were involved in model development. In the model fitting dataset, mean values of daily PM2.5 sulfate, nitrate, OC and EC are 1.63 μg/m3, 2.42 μg/m3, 2.12 μg/m3, and 0.59 μg/m3, respectively, accounting for 17%, 21%, 21% and 6% of average daily PM2.5 mass concentrations, respectively. This suggests that secondary inorganic mass and organic mass are the major constituents in PM2.5 in our study domain.After matching ground measurements of PM3.2.2.5 speciation. The adjusted R2 values of final models vary from 0.55 to 0.66 with the highest value for the sulfate model and the lowest value for the OC model, indicating that the GAMs developed in this study are able to capture majority of variability for the four PM2.5 species. The LOOCV R2 values decrease slightly by 0.04–0.06 compared to the model adjusted R2 values, showing high stability and little overfitting of the models. Comparing with the work of 2 values drop down to 0.14, 0.69 and 0.39 from 0.44, 0.74 and 0.72 for PM2.5 OC, sulfate and nitrate, respectively, indicating significant model overfitting for OC and nitrate likely due to a small sample size (2.5 species in this study may be attributed to that 1) the MISR fractional AODs are not designed to specifically match PM2.5 speciation, so they are unable to fully represent the physical and chemical properties of PM2.5 components; 2) we use column fractional AODs to predict ground level PM2.5 species without accounting for the vertical profile of aerosols, since the study domain is relatively small and the aerosol vertical profile data for the long study period and fine spatial resolution for this analysis was not available. Instead, we included PBLH as a covariate in models to partially reflect the effect of aerosol vertical profile on ground-level PM2.5 species concentrations; 3) other known and unknown indicators related to concentrations of PM2.5 species, such as industrial emissions, are not included in model development, which might also limit the capability of the models to predict PM2.5 species variability.ple size . The une2.5. EC is a strong light absorbing PM2.5 component; therefore MISR light-absorbing AOD14 with a single scattering albedo of 0.80 at 558 nm contributes significantly to the EC model as expected. However, MISR AOD2 representing non-absorbing aerosol is also positively related with PM2.5 EC concentrations in the model, which might be because MISR AOD14 itself is not sensitive enough to predict the variability of EC concentrations and MISR AOD2 could provide 2.5 species.MISR fractional AODs are significant predictors in all models. MISR AOD2, AOD3 and AOD19 are the most frequently used AOD components, probably because their sizes are highly consistent with that of PM2.5 species are displayed in 2.5 species concentrations with monotonically decreasing trends are removed from the model since it cannot be interpreted physically. Most of the MISR AOD components in the models show nonlinear relationships with PM2.5 speciation, as MISR AOD components are not defined to match the PM2.5 speciation exactly. The standard error curves (dashed lines) become nonconvergent at high values of MISR AOD, where fewer data support the model fitting and the estimates are less stable. PBLH shows a negative association with PM2.5 speciation that reflects the effect of vertical distribution on surface PM2.5 concentrations. Lengths of major roads are positively associated with concentrations of PM2.5 nitrate, OC and EC, reflecting the contribution of traffic emissions to PM2.5 nitrate, OC and EC.The smooth terms of the predictor variables in the fitted models of the four PM2.5 speciation at low pollution levels, where measurement errors may bias the association between PM2.5 speciation and MISR fractional AODs. Additionally, most measurements of low PM2.5 species concentrations are from IMPROVE monitors located in National Parks or rural areas, where the PM2.5 levels and variability are relatively small and difficult to predict using regression models. Additionally, the AOD retrieval error is another contributor to prediction uncertainty . These areas along the coastline with seaports have the highest PM2.5 sulfate concentration, particularly in Los Angeles area, indicating the contribution of ship emissions on sulfate pollution in Southern California. For nitrate, higher concentrations appear at areas with intensive road network and the Central Valley, suggesting that traffic and agricultural activities might be the major sources of nitrate precursor emissions. The hotspots of PM2.5 carbonaceous components appear in urban areas and along major roads, showing the potential influence of anthropogenic combustion and traffic emissions. There are some negative but near-zero predicted values of PM2.5 species after 2010 appearing in mountain and rural wilderness areas where particulate pollutant levels are too low to be predicted accurately with our models; however, population density is also low in those areas. Therefore, uncertainties in predicting the low concentrations of PM2.5 species may have minor influence on future policy assessment and health studies.rnia see , in part2.5 sulfate  and from IMPROVE network (http://views.cira.colostate.edu/fed) for remote/rural sites during 2001–2015. In our study domain, soil dust accounts for approximately 5%–10% of total PM2.5 mass in the South Coast Air Basin , where the excluded soil dust fraction is relative uniform.Daily PMir Basin , which i similar , but are similar . Therefo2.5 mass and reconstructed PM2.5 mass in MISR grids is displayed in 2.5 mass is slightly lower than the gravimetric measurements since soil dust is not included. The spatial pattern of reconstructed PM2.5 mass, however, is highly consistent with that of measured PM2.5 mass. The reconstructed PM2.5 mass concentrations based on predicted PM2.5 components are able to illustrate the spatial variability of total PM2.5 mass and capture the hotspots in city clusters of Los Angeles, Orange, Riverside, San Bernardino and San Diego counties, as shown by measured PM2.5 mass concentrations. The temporal variation of reconstructed PM2.5 mass is also in line with the measured values, showing that the PM2.5 mass concentrations decreased gradually from 2001 to 2012 and slightly rebounded during 2013–2015, which was partly caused by extreme drought conditions in Southern California (2.5 mass concentrations based on 15-year annual mean values in 2001–2015 in MISR grids are displayed in 2.5 mass concentrations. Hence, these comparisons indirectly confirm that the spatial patterns of predicted PM2.5 speciation concentrations are reasonable.Comparison of 15-year averaged annual measured PMlifornia . The sta3.5.2.5 species based on daily measurements by using total AOD instead of fractional AODs and compared the model performances with those based on fractional AODs. The results of GAMs with MISR total AOD of 4.4-km resolution are summarized in 2 and LOOCV R2 than those with total AOD for PM2.5 sulfate and nitrate; while the model performances with total AOD changes slightly for PM2.5 carbonaceous species comparing with those with fractional AODs. The results suggest that models with fractional AODs show improved capability in explaining the variability of PM2.5 species as compared with models with total AOD, especially for sulfate and nitrate.We also developed GAMs for the four PM3.6.2.5 sulfate, nitrate, OC and EC. Since this is a sensitivity analysis, we only processed the MISR 17.6-km data for 2007–2012. There are 180 matched site-days using MISR 17.6-km fractional AODs as compared to 302 site-days for MISR 4.4-km fractional AODs in the same study period. Results of model fitting and prediction maps of 2007–2012 are in 2.5 species’ concentrations, especially within urban areas; which is important to accurately assess exposure levels in epidemiological studies.In order to compare the predicted concentrations produced by current MISR aerosol products at 17.6-km resolution with the upcoming product at 4.4-km resolution, we also developed the GAMs following  based on3.7.2.5 at a fine spatial scale and capture their spatial variabilities and long-term temporal trends, this study has several limitations. First, the relatively small model fitting datasets may limit the capacity of the models to select predictor variables for predicting PM2.5 species concentrations, subsequently to affect model stability and prediction accuracy. Model stability and prediction accuracy are expected to improve with additional MISR data from neighboring paths in the study domain as more high-resolution data become available in the near future. Second, our GAMs assume that the associations between MISR AODs and PM2.5 speciation concentrations are constant across the entire modeling domain and 15-year time period. Given our relatively small study domain, this assumption may not cause substantial prediction error. Third, although the spatial resolution of PM2.5 speciation prediction maps is as high as 4.4 km, it is not feasible to study daily PM2.5 speciation concentrations based on GAMs developed in this study due to MISR’s low sampling frequency and missing data due to cloud cover. However, the results show good agreements between annual predicted concentrations and annual observed concentrations of PM2.5 speciation. Hence, the annual predicted concentrations of PM2.5 speciation are able to capture long-term variations of PM2.5 speciation. In addition, we used column MISR fractional AODs as the main predictors for surface PM2.5 speciation concentrations without considering aerosol vertical distribution. Satellite-estimated PM2.5 concentrations have been proven to be well correlated with ground observations in a well-mixed boundary layer and under clear sky conditions (2.5 and AOD can be improved by considering the vertical profile of aerosols (2.5 speciation concentrations and satellite AOD (2.5 speciation concentrations and MISR fractional AODs might PM2.5be the future research direction, when aerosol vertical profiles with a high spatial resolution and accuracy become available. Finally, as is true for all statistical models, the GAMs developed in study are specifically tuned for the Southern CA domain, which may not perform as well if applied to other regions.Although the analysis suggests that the GAMs developed based on MISR fractional AODs can be used to predict the major chemical components of PMnditions ; therefoaerosols . Currentlite AOD . Scaling4.2.5 sulfate, nitrate, OC and EC by using MISR 4.4 km-resolution prototype fractional AOD components at the sub-regional scale. Our predictions of PM2.5 speciation based on the current modeling strategy have higher accuracy compared to previous CTM simulation results, and have a finer spatial resolution compared to results based on MISR 17.6 km-resolution product. Our analysis suggests that this data product, coupled with the modeling approach described here, is able to explain the most variability of concentrations of major PM2.5 chemical components in Southern California. The 4.4 km-resolution MISR fractional AODs based predictions successfully capture hotspots and finer gradient variability of the four PM2.5 species within our study region. Distinguishing the high spatial variability of PM2.5 species is also crucial for exposure assessment in health studies. Despite the low daily sampling frequency of MISR aerosol product, the annual concentrations of PM2.5 species are well represented by the predicted annual concentrations based on the models developed in this study. The long-term mean predictions based on MISR fractional AODs provide a promising way to estimate concentrations of PM2.5 species, which will be valuable for understanding the effectiveness of air pollution control policy and supporting epidemiological studies by linking estimated PM2.5 speciation data with common spatial units such as census tract or zip code. Further predictions of PM2.5 speciation concentrations at the local to global scales will be possible when Version 23 MISR aerosol product is publicly released in the near future.This analysis is the first attempt at producing prediction surfaces of PMSupplemental