Okara, an unassuming residue, is emerging as a notable reservoir of essential nutrients, encompassing an abundant supply of protein, dietary fiber, and potent antioxidant components. Hence, the incorporation of okara as an ingredient in the production of rice flour-based rolls held a considerable interest in nutritional and functional aspects. Okara flour supplement was prepared by drying at 100 °C for 2 h and selected based on the highest antioxidant level. Gluten-free rolls were prepared containing 0, 5, and 10% okara flour dried at 100 °C for 2 h, and their physicochemical properties were analyzed. Okara flour addition reduced the deformation of gluten-free batter roll by improving solid and liquid-like behavior, as evaluated with rheological measurements. Rolls containing okara flour processed at 100 °C for 2 h had increased firmness and decreased specific volume compared to the control. However, there were no significant differences in the sensory evaluation scores, suggesting that the consumers’ acceptance of the control and the Okara rolls was similar. Okara flour supplement at 10% addition led to the nutritional improvement of the gluten-free rolls . In contrast, there were no significant differences in the antioxidant level compared to the control. Okara flour is a functional ingredient with potential use in gluten-free products and a variety of novel products where enrichment is desirable.  Glycine max (L.) Merr.), is a byproduct from soybean products such as soy milk and tofu that contains essential nutrients associated with health [Okara, soy pulp  using a blender  for 3 min. The soybean pulp (okara) was filtered with a double-layer cheesecloth. The okara was dried with three temperature-time treatments: 70 °C for 4 h, 80 °C for 3 h, and 100 °C for 2 h; the treatments were selected from a preliminary study. Dried okara was ground in an electric grain mill , screened to pass a 250 μm sieve particle sizes (60 US mesh screen), and stored in sealed polyethylene bags until needed for analyses. v/v) mixture was done. The supernatant (0.1 mL) from centrifugation  was used to react with 0.1 mM methanolic DPPH solution (3.9 mL). Then, the assay mixture was incubated and shaken for 30 min at ambient temperature in a dark place. The mixture sample was analyzed for an absorption at 517 nm using UV/Vis Spectrophotometer  [Each temperature-time treatment of okara flour was analyzed following AACC Approved Methods of Analysis for moisture 44–17.01) , ash  . In addiA commercial rice flour  was used for gluten-free rolls. The rice flour composition was 6.9% protein, 1.3% fat, 0.5% total dietary fiber, and 79.8% carbohydrate by difference. Okara flour from the 100 °C and 2 h treatment was selected for its highest level of antioxidants, whereas the protein and total dietary fiber were not significantly different in the three treatments . Okara fProximate analyses of gluten-free roll with okara were performed with AACC Approved Methods for moisture content (44-31.01) , ash  equipped with a Peltier plate controlling the temperature to 25 °C. The parallel plate (25 mm cross-hatched geometry) was lowered down to a batter sample with a 1 mm gap . The batJ (Pa−1) was measured for deformation during the creep and recovery test as a function of time. Compliance is a ratio of recorded creep strain (γ) to constant shear stress (σ) or equal to 20 Pa for this study, as shown in Equation (1).The sample was prepared as described in The experimental data obtained from this test was fitted into a 6-parameter Kelvin-Voigt model (Equation (2) for creep phase and Equation (3) for recovery phase) . During 3) was measured using rapeseed displacement. Specific volume (cm3/g) was also calculated using loaf volume (cm3) divided by loaf weight (g). Crumb color of gluten-free roll and batter were also analyzed with a HunterLab ColorFlex . Parameters of L* , a* , and b*  values were recorded [Gluten-free rolls with three different levels of okara were prepared as described in recorded . MeasureGluten-free rolls were cooled for 1 h after baking, sliced into 1 cm thickness, and stored in ziplock plastic bags. The gluten-free roll crumb firmness was measured by texture profile analysis (TPA) using a TA-XT2 texture analyser  (50 kg load cell) with a 25 mm diameter cylindrical probe. The conditions were set as pre-test speed, 2.0 mm/s; crosshead speed, 1 mm/s; post-test speed, 5.0 mm/s and compression to 40% height . MeasureSensory analysis of gluten-free rolls was performed with a consumer acceptance test the day after baking (day 1 of storage) . The samThe experimental design for this study was a completely randomized design (CRD). Analysis of variance (ANOVA) procedures were used, assuming a model in a completely randomized design using the SAS program . The mean significant difference was tested using Tukey’s multiple comparison test (α = 0.05). The multiple analysis by ordination called principal component analysis (PCA) using Canoco for Windows 5 software  was applied to measure correlation among parameters and samples . p < 0.05); in contrast, there was no significant difference in carbohydrate, fat, and protein (dry basis). The total dietary fiber and moisture content were lower after heating at the higher temperature for a shorter time (p < 0.05). A previous study found that using the 40% power level of a microwave oven to dry okara flour could also reduce the moisture content of okara up to 1 h for processing. The beginning of the water evaporation process happened when the okara temperature reached 91 °C [w/w). Using 100 °C for 2 h for drying okara flour yielded the highest level of total antioxidant (1.35 times higher than using 70 °C for 4 h) (p < 0.05). Thus, the combination of time exposure to heat and temperature level significantly affected the antioxidant level of okara flour. The study of the total phenolic content (TPC) of microwave-processed pasta with 10–50% of okara flour showed that the TPC can be varied, from 158.37 to 232.90 mg (GAE/100 g) [p < 0.05). The properties of okara flour at different temperature levels and times were shown in ed 91 °C . In thised 91 °C . The aveE/100 g) . In a sip < 0.05). Thus, the gluten-free rolls could be claimed as high-fiber products (according to conditions for nutrient content claims for dietary fiber from FAO). However, adding okara at 5% in the gluten-free roll did not improve protein content. There were no significant differences in total antioxidant in gluten-free rolls containing up to 10% okara flour compared to the control   into gluten-free rolls to improve the nutritional value. Using 10% okara flour in gluten-free rolls significantly increased total dietary fiber  and protein content , compared to control (0% okara flour addition) . The toty basis) . This suThe Burgers model has been applied to study rheological changes related to the internal structure and changes in gluten-free cake batter systems using mechanical models ,27,28,29p < 0.05) (p < 0.05). Thus, the addition of okara strengthens the structure of gluten-free batter rolls. Zero shear viscosity improved significantly after adding okara by up to 70.6% (p < 0.05). There was a significant decrease in retardation time in both creep and recovery phases by up to 18.7% (p < 0.05). The elements of the Burgers model could be attributed to the entanglement of long-chain polymers in the material structure. The addition of okara flour and its consequent increase in total dietary fiber and protein could reinforce the batter structure of gluten-free rolls by increasing the hydrogen bonding with water around its particles. Okara insoluble fiber structures normally consist of cellulose, hemicellulose, and lignin [The addition of okara to gluten-free roll batter resulted in a significant decrease in deformation, increase in recoverability, and decrease in flowability ( < 0.05) . The Burd lignin . The stud lignin . p < 0.05) . Storage modulus (G’) was higher than loss modulus (G”) in all samples throughout the frequency range, suggesting that gluten-free batter rolls with and without okara had solid-like properties . An incr < 0.05) . In addip < 0.05)  is a physical measurement designed for mimicking mouth biting action by compressing a sample twice in order to evaluate the sample as it is chewed. The results showed that okara flour increased hardness of gluten-free crumb rolls by up to 155% at 10% okara flour addition; on the other hand, the springiness was lower by up to 53.6% compared to gluten-free crumb rolls without okara ( < 0.05) . There wp < 0.05). In agreement with the study of okara incorporation in gluten-free cookies, a decrease in the specific volume of gluten-free cookies was obtained after adding okara in higher amounts [p < 0.05). In addition, the overall characteristics of gluten-free rolls with okara flour (dried at 100 °C for 2 h) were evaluated using the hedonic test and are shown in Gluten-free roll samples enriched with okara flour were tested, and their characteristics are reported in  amounts . The imp amounts . General amounts . Thus, oct1 and rt2) were independent of other parameters; on the other hand, tr1 moved closely to deformation parameters. The result of relaxation time showed the same trend as the modeling in gluten-free red kidney bean cupcake batter [The biplot graph shows the separation between samples from gluten-free rolls with different levels of okara flour in a very high variation with 99.6% total explained variance . The prie batter . The oveOkara dried at 100 °C for 2 h showed the highest antioxidant value, which was 26.2% more than drying at 70 °C for 4 h. Thus, this process condition was chosen for drying fresh okara and making gluten-free rolls. Using okara flour at 0, 5, and 10% revealed that okara flour impacts the viscoelastic properties of batter as well as the physical properties of a gluten-free roll. The gluten-free roll batter had lower flowability and deformation after adding okara flour at 5 and 10% compared to control. The gluten-free roll with 10% okara had the highest hardness crumb and the lowest value in specific volume. The okara treatment of 100 °C for 2 h produced the highest antioxidant level but did not survive the baking process of the gluten-free rolls with the addition of 10% of okara flour. A supplementation of okara at a higher percentage with the incorporation of biopolymers should be focused on in future work toward food-waste reduction efforts.


Brucellosis is a zoonosis of major public health and economic importance that is endemic in livestock in Ethiopia with varying levels of seroprevalence.A cross-sectional study was carried out to determine the individual and herd-level seroprevalence of brucellosis in small ruminants in the Korahey zone of the Ethiopian Somali Region. A total of 324 sera from 63 herds of small ruminants were collected randomly using a multistage sampling technique and the sera were tested using the Rose Bengal Plate Test, and seropositive reactors were confirmed by the Complement Fixation Test.p > 0.05) with Brucella seropositivity at individual and herd level seroprevalence during multivariable logistic regression analysis. Pastoral community awareness regarding the public health impact of brucellosis and the promotion of an intersectoral One Health approach for the effective control of brucellosis is recommended.The seroprevalence of brucellosis at the herds and the individual level was 6.35% (95% CI: 0.0–13%) and 1.23% (95% CI: 0.0–2%), respectively; with 1.4% in goats and 0.9% in sheep. Moreover, predicted variables like age group, parity, history of abortion, fetal membranes, herd size, ownership of other livestock species, contact with wild animals in the past year, the introduction of new animals in the past year, and lending of breeding males in the past year were not significantly associated ( The major production system in the zone is pastoralism, with some mixed livestock crop production practiced around Kebridahar. The livestock population of the zone is 3,576,492, consisting of 311,243 cattle, 1,323,491 goats, 1,265,585 sheep and 582,860 camels  per woreda (districts) based on livestock population and accessibility. The distribution of the herds across the kebeles and woredas was determined based on the estimated population size of the respective kebeles.2.6.Approximately 10 mL of blood sample was collected from the jugular vein of each study animal using plain vacutainer tubes, needle holders, and needles. The blood sample from each animal was labeled and centrifuged at 10,000 rpm for 3 min, and sera were removed by siphoning them into sterile cryovials. The serum samples were then transported to the Jigjiga regional veterinary diagnostic and research laboratory in an ice box, where they were stored and kept at −20°C until serology procedures were performed.2.7.The screening procedure with RBPT was performed at Jigjiga Regional Veterinary Diagnostic and Investigation Laboratory and National Veterinary Institute (NVI) at Bishoftu, whereas CFT was conducted at NVI using test protocols as outlined by OIE  and the Brucella abortus strain 99), control sera, and other reagents were obtained from Atlas Medical, William James House, Cowley Rd. Cambridge Cb4, 4 WX and sensitized sheep red blood cells (SRBCs) were obtained from the NVI. The serum specimens were tested serially first using RBPT and then CFT for those that tested positive for RBPT. An animal was considered positive if the serum specimen tested positive on both RBPT and CFT, whereas a herd was considered positive if at least a single serum specimen from an animal within the herds tested positive on both RBPT and CFT. For RBPT, the Rose Bengal test antigen was prepared from the killed standard strain of B. abortus strain 99 and stained with Rose Bengal dye in an acidic buffer pH 3.65.The CFT and RBPT test antigens  and stepwise backward elimination procedure, dropping the least significant independent variable until all the remaining predictor variables were significant. The statistical significance level was set at 95% confidence level and 5% level of precision so that p ≤ 0.05 was considered significant.Serological data were entered into a Microsoft Excel Spreadsheet version 2016 (Microsoft Corporation) along with the corresponding data generated with the questionnaire. The statistical software package SPSS version 20  was used for data analysis. Descriptive statistics like frequency and proportion were employed for the description of the seroprevalence of the disease. A herd level and individual animal seroprevalence were calculated by dividing the number of positive test results by the total number of herds and animals sampled, respectively. Univariable analysis using Fisher’s exact test was used for the analysis of the association between individual animal-level and herds 3.3.1.A total of 324 animals composed of 213 (65.7%) goats and 111 (34.3%) sheep were sampled. These animals were sampled from 63 herds from 9 kebeles (peasant associations) in Doboweyn, Kebridahar, and Sheygosh districts of the Korahey zone, Somali Region of Ethiopia. The 63 herds had a total of 4,026 animals, of which 2,512 were goats and 1,514 were sheep. The median sheep and goat herds’ size was 56 . One-third of the herds in the study (33.3%) had more than 60 heads of sheep and goats, and the remaining 66.7% had a herd size of 60 heads or below.3.2.3.2.1.Brucella antibodies on RBPT. Of the samples positive for RBPT, 2.3% (95% CI: 0.0–4%) were from goats, and 0.9% (95% CI: 0–3%) were from sheep. Of the samples testing positive on RBPT, 1.23% (95% CI: 0.0–2%) tested positive on CFT, of which 1.4% (95% CI: 0.0–3%) were goats and 0.9% (95% CI: 0–3%) were sheep. In this study, the animal-based seroprevalence of small ruminants brucellosis in three district of the Korahey zone of Somali regional state was 1.85% by RBPT and 1.23% by combined RBPT and CFT. Thus, the overall seroprevalence was 1.23% and were taken for subsequent data analyses  were positive for analyses .3.2.2.Based on CFT, the highest seroprevalence of 5.9% (95% CI: 2–14%) was recorded in the Dalad kebele of Kebridahar district, followed by 2.9% (95% CI: 3–9%) in the Harano kebele of Doboweyn district and then by 2.6% (95% CI: 3–8%) in the 01 kebele in Sheygosh town of Sheygosh district. In the remaining 6 locations, namely, 01 kebele of Kebridahar town and Bundada kebele of Kebridahar district; 01 kebele of Doboweyn town and Jidhale kebele of Doboweyn district and Harir and Wijiwaji kebeles of Sheygosh district, zero seroprevalences were recorded.3.2.3.Brucella species. All the herds sampled had a mixture of sheep and goats; hence, comparing herds’ seropositivity levels by species was deemed irrelevant.Of the 63 herds sampled, the overall true herd seroprevalence was 6.35% (95% CI: 0.0–13%). None of the herds had more than one animal testing positive for 3.3.3.3.1.Brucella seropositivity, several factors were examined in the bivariate analysis. These include species, sex, age, number of parties, previous history of abortion and/or retained fetal membrane, and previous history of orchitis and epididymitis.In determining the risk factors associated with individual animal p > 0.05).In terms of sex, 276 (85.2%) were females, and 48 (14.8%) were males. All the seropositive animals were females, but there was no statistically significant difference in the brucellosis seroprevalence between the two sexes (p = 0.001) was observed between the different age categories with regard to Brucella seropositivity, with the highest prevalence being in the >4-year-old age category (16.7%), followed by the category aged 3–4 years (0.5%). None of the animals in the age range of 1–2 years tested positive for Brucella.The majority, 196/324 (60.5%), of the sampled animals were between 3 and 4 years old, and only 18 (5.5%) were older than 4 years. A significant difference  gave birth at least once, with 13 (4.7%) of them being primiparous, giving birth only once. Most of the female animals (84.8%) gave birth between 2 and 4 times. Only 11 females (4%) gave birth more than four times. The highest seroprevalence was observed in the category of female animals that gave birth more than 4 times, with 3 of the 11 female animals in this category (27.3%) showing seropositivity with CFT. This was followed by the females that gave birth between 3 and 4 times, in which a seropositivity prevalence of 0.6% was recorded.p < 0.05) between animals with a history of previous abortion and those without a previous history of abortion , of the female animals sampled had a history of abortion at least once in the past, whereas 49/276 (17.8%) of female animals experienced retained fetal membranes. Regarding male animals, 11/48 (23%) had a history of experiencing either orchitis or epididymitis, but this study did not find any seropositive males. All the seropositive animals had a previous history of abortion and retained fetal membranes, and it was found that there was a significant difference .One-third of the herds  had more than 60 heads of sheep and goats, and the remaining herds had a maximum herd size of 60 sheep and goats. The herds with a size of more than 60 animals had almost 7 times higher odds of having positive reactors for Brucella (75%) came from herds whose owners also keep other livestock species, and even though it is not statistically significant, sheep and goat herds belonging to owners who also keep other livestock species have 5 times more odds of getting brucellosis compared to other herds whose owners do not keep other livestock species as well .Of the 63 sampled sheep and goat herds, 25 (39.7%) are kept together with other livestock species, as the owner also keeps other species of livestock apart from goats and sheep, and the other 38 (60.3%) households possess only small ruminants. Almost all of the respondents, 24/25 (96%), who also own other livestock species raise the different herds separately, and only one respondent said that they house the different species of animals together. Most of the other livestock species kept in the area are camels, cattle, and donkeys ordered in terms of population. Three of the herds with positive reactors to Brucella were from those herds that lacked the calving pen, but this was not statistically significant (p > 0.05). Since the area is predominantly pastoralist with grazing lands shared communally, there is a high chance of contact between different herds. In the present study, 52/63 (82.5%) of the herds came into contact with other herds of sheep and goats in the past year, but the current study did not find a statistically significant association between Brucella herds seropositivity and contact with other herds .Nearly all of the herds 62/63 (98.4%) did not have a calving/lambing/kidding pen where animals delivered offspring, and all the herds with positive reactors to Brucella .On the other hand, 14/63 (22%) of the herds came into contact with wild animals in the past year. The most commonly encountered wild animals were antelopes such as dik-dik, warthogs, foxes, and hyenas. Although it was not statistically significant, herds that came into contact with wild animals had a higher chance of having positive reactions to Brucella, 3 (75%) had experienced abortion in the past year compared to 29 (49%) in seronegative herds. Herds with abortion reports in the past year had an approximately 3 times higher chance of containing seropositive reactors than herds that did not experience abortion in the past year, but this difference was not significant .Abortion in sheep and goat herds was reported in 32/63 (50.8%) of the herds in the past year. Of the 4 herds that tested seropositive for Brucella infection .New animals were introduced into 14/63 (22.2%) of the herds in the past year mainly through purchases and gifts from relatives. Comparing herds that introduced a new animal into the herds in the past year to those herds that did not, the study found that herds that introduced new animals into the herds had 13 times increased chances of having seropositive reactors for p = 0.026).Sharing of breeding males is less common, and only 13/63 (20.6%) of the herds have lent their breeding male sheep or goat animals over the last year. Herds that lend male animals for breeding purposes have a more than 14-fold increased chance of having seropositive reactors compared to herds that do not lend their male animals to other herds for breeding. This was statistically significant  in herds’ seropositivity was observed between the herds in relation to vaccination and receiving veterinary advice (Although 26/63 (41.3%) of the herds were vaccinated in the past year, no vaccination was given against brucellosis, and only 19 (30%) respondents had received/obtained veterinary advice last year, with the majority, 44 (70%), not getting veterinary advice of any kind in the past year. No statistically significant difference (y advice .3.3.3.p ≤ 0.25), herds’ size, ownership of other livestock species, contact with wild animals in the past year, the introduction of new animals in the past year, and lending of breeding males in the past year were selected for the multivariable analysis using a binary regression model. Additionally, age category, previous history of abortion, number of parity and retained fetal membrane history were selected for multivariable regression model from individual seropositive animal level factors. Nevertheless, none of the variables showed a significant association (p > 0.05) in the multivariable analysis . The result of this study is in agreement with other findings by Tewodros and Dawit (p < 0.05) difference in Brucella seroprevalence between the two sexes. The lower number of males (n = 48) sampled and tested compared to a higher number of females (n = 276) in the sample may have contributed to the higher female animal seropositivity. It is also possible that male animals are less likely to contract Brucella infection because they do not contain erythritol  higher seroprevalence of small ruminant brucellosis in the old age group than in the medium and young age groups. This is consistent with the findings of Tsegaye et al.  than in those without these problems. Similar findings were reported by Wedajo et al.  herds’ sizes than in small (≤60 heads), but the difference was not significant. This is in line with the findings reported by Wedajo et al.  in the AThe introduction of new animals from unscreened herds into sheep and goat herds was a major risk factor observed in this study. Pastoralists usually introduce these animals into the herds as replacement stock through purchases, gifts, or donations from relatives. This finding is consistent with the findings of several authors who discovered that the introduction of animals from non-free Brucellosis herds or herds with unknown Brucellosis status was a major factor associated with Brucellosis in sheep, goat, and cattle herds , 54–57.Other research suggests that introducing infected animals can increase individual-level prevalence because the longer they are in contact with the rest of the herds, the greater the risk of spread , 58. AniBrucella and the presence of female animals that had recently given birth. Brucellosis causes late-term abortions, which increases the risk of the disease spreading to other animals in the herds while they graze on the contaminated pasture lands because aborting animals typically shed the bacteria into the environment. Abortion represents the major complaint attributed to Brucella infections in livestock . The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.AMH, FA, AHH, AM, and HG contributed to data collection, study design, and interpretation, manuscript draft, and writing. AMH, FA, and AHH contributed to the conception of the research idea, study design, data analysis, writing, revising and editing, the design of the study, and data interpretation. AM and HG contributed to the reference search and manuscript writing and editing. All authors approved the submission of the final manuscript.This study was funded by the Jigjiga University Research and Community Service Vice President Office.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Therefore, the reported fluorescence properties of PEG should be taken with a grain of salt, warranting further investigation.Polyethylene glycol (PEG) is a polyether compound commonly used in biological research and medicine because it is biologically inert. This simple polymer exists in variable chain lengths (and molecular weights). As they are devoid of any contiguous π-system, PEGs are expected to lack fluorescence properties. However, recent studies suggested the occurrence of fluorescence properties in non-traditional fluorophores like PEGs. Herein, a thorough investigation has been conducted to explore if PEG 20k fluoresces. Results of this combined experimental and computational study suggested that although PEG 20k could exhibit “through-space” delocalization of lone pairs of electrons in aggregates/clusters, formed via intermolecular and intramolecular interactions, the actual contributor of fluorescence between 300 and 400 nm is the stabilizer molecule, i.e., 3- One common application of PEG is as an osmotic laxative.5 Also, PEG is often attached to drug molecules in a process called PEGylation to enhance their stability and solubility, decrease the immune response, and increase circulation time throughout the body.6 Recently, PEGylated lipids have been included as an ingredient in the vaccines for COVID-19, and PEG allergies in patients are suspected to be the likely cause of the rare anaphylactic response to the vaccine.3 Additionally, PEG is used to coat surfaces to serve as a lubricant or a preservative.8 Moreover, PEG molecules of variable sizes are commonly used for studying the effects of molecular crowding and confinement on the conformation and function of proteins and nucleic acids.10 While probing the conformational change of a protein in crowded “intracellular-like” environments using intrinsic fluorescence spectroscopy, it was observed that the solution crowded with PEG of 20 kDa molecular weight (PEG 20k) fluoresced even in the absence of the protein under investigation; the fluorescence intensity of an aqueous solution containing phosphate buffer, PEG 20k, and sodium chloride was abnormally high. Unlike traditional fluorophores, which possess conjugated π-systems, PEG does not have such structural characteristics but interestingly fluoresced regardless.Polyethylene glycol (PEG) is a flexible, non-toxic polymer.14 When in an aggregated state, heteroatoms are brought into proximity so that their lone pairs of electrons are easily delocalized. Additionally, the aggregation creates a more rigid structure, thereby reducing non-radiative relaxation.15 In a recent study, the PEG of molecular weights from 2k to 12k were found to be emissive.14 Moreover, it was reported that PEG 8k has the maximum emission among PEG 2k to 12k at 340 nm when excited at 280 nm. The fluorescence property of PEG 20k, however, remained unexplored. Various experimental studies revealed aggregate formation that might induce intrinsic fluorescence properties to PEG molecules via “through-space” delocalization.14 However, the exact mechanism of the fluorescence has remained elusive. In the present study, we report a comprehensive investigation of the molecular basis of the fluorescent properties of PEG 20k molecules using combined experimental and computational methods. Fluorescence measurements of PEG 20k were conducted in aqueous solutions as well as in the presence of metal ions. Also, atomic force microscopy (AFM), dynamic light scattering (DLS), fluorescence anisotropy, and proton nuclear magnetic resonance (1H NMR) were used to analyze PEG 20k aggregates, assess their sizes, and investigate the presence of delocalization. Atomistic molecular dynamics (MD) simulations were performed to explore the structural properties of PEG 20k. Quantum chemical calculations were performed at the level of density functional theory with improved functionals18 to probe if the “through-space” delocalization of the PEG 20k aggregates gives rise to the fluorescence properties.Based on recent studies, it was revealed that PEG can be classified as a nontraditional intrinsic luminophore, and the resulting luminescent properties are due to the phenomenon referred to as aggregation-induced emission (AIE).2, CoCl2, FeCl3, CrCl3, and K2CrO4. Blank samples containing only water and the corresponding metal salt were prepared for each of the six ionic compounds. PEG 600, PEG 2k, and PEG 8k were purchased from Thermo Fisher Scientific and were used as received without further purification. PEG 20k was obtained from Thermo Fisher Scientific and MilliporeSigma. Fluorescence of PEG 20k was recorded before and after purification with diethyl ether. Samples were excited within a range of 210–300 nm, and the emission spectra were recorded from 300 to 400 nm with 5 mm spectral slits. All experiments were performed using a quartz cuvette with a 1 cm optical path length and were done in triplicate.Steady-state fluorescence experiments were performed using an Agilent Cary Eclipse fluorescence spectrophotometer. Samples of various concentrations of PEG 20k and other variable-sized PEG molecules were prepared by dissolving the required quantity of PEG in water or water along with 100 mM NaCl and 30 mM phosphate buffer of pH 7.4 to mimic biological conditions. Solutions of 100 mg/mL PEG 20k in the presence of six different metal ions were prepared using 10 mM NaCl, MgCl2O control was run alongside the PEG 20k duplicates.13 mL of 400 mg/mL PEG 20k sample (purified before and after washing with diethyl ether 6 and 12 times) was pipetted from the original sample into a 40 mL brown septum vial with a lid, and a Monotrap monolithic disc coated with octadecyl silane activated carbon  was floated on top. Samples were then incubated in a water bath at 37 °C for 1 h. Following removal from the water bath, the Monotrap disk was removed from the vial, patted dry with a Kimwipe, and gently placed into a 3.0 mL microreaction vial with a lid. 2.0 mL of mass spectrometry (MS)-grade dichloromethane (DCM) was added, and the lid was tightly shut. The samples were put into the sonication bath for 10 min. All the remaining DCM was then extracted from the microreaction vial and placed into 1.5 mL Agilent screw cap vials and labeled. The compounds were separated and detected using an Agilent Technologies gas chromatography system (model: 7820 A GC System) with a quadrupole mass spectrometer. The instrument used a J&W DB624 GC column, 60 m, 0.32 mm, 1.80 μm, 7 inches. The oven temperature was set to 40 °C for 3 min and then increased to 320 °C at a rate of 9 °C/min. The front inlet temperature was 250 °C. The injection was in the split mode with a 1:1 ratio and 1 μL of injection volume at 7.14 psi. The detector temperature was 230 °C. The detection and data acquisition were performed in the scan mode at 35 scans/s, from 35 to 450 Da. Peaks were identified by comparison with the National Institute for Standards and Technology (NIST) database. All samples were done in duplicate, and a deionized HSamples were prepared by applying 100 μg/mL, 20 mg/mL, 200 mg/mL, and 300 mg/mL PEG 20k solutions to freshly cleaved pieces of highly oriented pyrolytic graphite (HOPG) as to cover the whole surface of the HOPG. Samples on HOPG were incubated for 30 min. After 30 min, excess PEG 20k solution was removed from the HOPG substrate with a dry Kimwipe by capillary action. The sample was then dried in vacuo for 30 min and scanned using the MFP-3D Origin atomic force microscope from Asylum Research. Scans were conducted in a 5 μm area using the uniqprobe qp-BioAC probe and a force constant of 0.15–0.55 N/m. The scan rate was maintained at 0.4 Hz, and the scans were done with 512 scan points and lines. All samples were run in duplicate.To examine if there is a formation of vesicles of PEG 20k in water and assess the distribution of the hydrodynamic radius of those vesicles, the DLS experiment was conducted using a Wyatt Mobius in the DLS-only mode with disposable cuvettes at 20 °C. Standard correlation cutoffs were used with auto-attenuation on and normal laser mode with a laser wavelength of 532.0 nm and a detector angle of 163.5°. An acquisition time of 2 s was used, and 10 runs were averaged per sample set. Each sample was duplicated 5 times to determine the statistical consistency of the data. The built-in Wyatt-developed regularization fit was used to determine the average hydrodynamic radius assuming multiple peaks in solution with data plotted in radius and percent intensity with a random coil model. Samples of 100, 200, and 300 mg/mL PEG 20k were each prepared with 100 mM NaCl and 10 mM phosphate buffer, pH 7.4, and filtered using VWR brand PES membrane 0.2 μm syringe filters.Anisotropy can reveal information about the size, shape, and rigidity of a sample in solution. Samples of 100, 200, and 300 mg/mL PEG 20k were each prepared in 100 mM NaCl and 10 mM phosphate buffer, pH 7.4. The anisotropy measurements were conducted with the automated polarizer accessory of the Agilent Cary Eclipse fluorescence spectrophotometer. Samples were excited at 288 nm because it was found to give the highest emission intensity recorded from 320 to 340 nm at temperatures of 25 and 50 °C.2O at different concentrations. Solid PEG 20k was dissolved in D2O to prepare 20 and 200 mg/mL solution. 1D-1H NMR spectra were recorded using the Bruker Avance II 400 MHz NMR spectrometer.The NMR experiment was conducted to analyze the chemical shift of the ethylene group of PEG 20k in DThe samples (30 and 300 mg/mL of PEG 20k) were prepared by dissolving the required amount of solid PEG 20k in deionized water. The spectra were recorded from 190 to 900 nm using the Varian Cary 50 UV–Vis spectrophotometer.19 All MD simulations were performed using GPU-enabled nanoscale MD (NAMD).21 Calculations were performed on the hybrid GPU–CPU BOSE supercomputer with 61 nodes and 3904 cores of the Blugold Center for High-Performance Computing, University of Wisconsin-Eau Claire. Each node of the cluster contains two CPUs, equipped with 2.3 GHz/32-core AMD EPYC 7452, and is connected through Hewlett Packard Slingshot internode connection. Additionally, GPU nodes have NVIDIA Tesla V100S 32GB GPU cards that enable NAMD to have hardware acceleration using GPU or CPU vectorization.Visualization and molecular editing were carried out using the visual MD (VMD) program suite.23 was employed for molecular mechanical calculations. Non-bonding interactions were modeled using a switching function with a “switchdist” of 10 Å, a cutoff of 14 Å, and a “pairlistdist” of 16 Å. Furthermore, electrostatic interactions were treated with the particle mesh Ewald method.24 The leapfrog Verlet algorithm25 with a time step of 2 fs was used to compute atomic velocities and displacements. A modified Nosé-Hoover method27 was employed for conformational sampling, where fluctuations in the barostat were controlled using Langevin dynamics.29 For all solvated calculations, a periodic boundary condition, which controls the pressure by dynamically adjusting the unit cell volume and rescaling atomic coordinates, was used.The CHARMM36 force field31 Gas- and solvation-phase calculations were performed on Gaussian16 with the M06-2X density functional16 using the 6-31+G basis set.32 Implicit solvation calculations were performed using the continuum solvation model, SMD.33 Time-dependent density functional theory (TD-DFT)34 was used to compute the excited state and model the electronic absorption of the PEG clusters as well as the monomeric EG. In these calculations, ground-state geometrically optimized structures were used to compute the excited-state potential energies using the Coulomb-attenuating method Becke 3-parameter, Lee–Yang–Parr (CAM-B3LYP) functional37 along with the 6-311++g basis set.32Electronic structure calculations were carried out using density functional theory.22 suite using homemade scripts. At first, ethylene glycol (EG) and its dimer were built. A major axis along the dimeric EG unit was defined, and that unit was copied and translated along the axis several times. These units were patched together using a homemade script to build a large chain. The final PEG 20k molecule had a molecular weight of 19,963 Da. The linear construct of the PEG chain was subjected to 100 ns MD simulation. A compact structure of PEG 20k was formed, which was solvated, and the resultant system was minimized for 50 ps. This minimized system was subjected to MD simulation for an additional 100 ns.A PEG 20k molecule was constructed with the CHARMM programThe linear PEG 20k structure was duplicated 6 times to form a heptamer. The heptamer was first minimized, and then, a 200 ns MD simulation was performed in the gas phase. Upon gas-phase equilibration, the heptameric system was solvated at neutral pH. This resultant system was simulated for an additional 200 ns.31 Briefly, spherical regions of 5, 7.5, and 10 Å radii were extracted from the classical mechanically equilibrated structures and stored in the MD simulation trajectory of the PEG 20K monomer. Hydrogen atoms were added to complete the valences of the carbon and oxygen atoms. The smaller clusters were geometry-optimized using the smaller basis set 6-31+g. Excited states were computed from these optimized ground-state geometries using the expanded basis set of 6-311++g. The excited states for the cluster of 10 Å radius could not be determined because of the lack of convergence. A separate calculation of atomic charges was carried out on the ground states using natural bond orbital population analysis, which computes the localized electron density from the optimized electronic wavefunctions.38To examine the structure, electron delocalization, and bonding of the PEG 20k interior, a quantum chemical study was conducted at the level of density functional theory.ex)  dissolved in buffer  versus dissolved in water alone did not display a significant difference in emission intensity. Also, PEG 20k from Thermo Fisher Scientific and MilliporeSigma exhibited similar fluorescence behavior .The wavelength of maximum emission was examined by measuring the fluorescence of PEG 20k for a range of excitation wavelengths (λex) 1. The aqex = 295 nm , it led us to investigate if any chemicals were present as impurities in the commercially available PEG 20k.The fluorescent properties of PEG of various molecular weights were compared by exciting the PEG samples at 295 nm. Only PEG 20k displayed a significant fluorescence under the experimental condition, i.e., 100 mg/mL PEG in the presence of 100 mM NaCl and 10 mM phosphate buffer, pH 7.4, and λ= 295 nm 2. This wght PEGs 2 and S2,tert-butyl-4-hydroxyanisole (3-BHA) was observed –MS experiments were carried out. For the unpurified PEG 20k, a single peak was observed at approximately 18 min in the GC–MS chromatogram 3. After observed 3a. A valobserved 3b,c. Theobserved 1. The anobserved 1. These observed 1. This d14 Therefore, further studies were conducted to examine the possibility of PEG 20k to fluoresce, especially focusing on the aggregate formation and the “through-space” delocalization of lone pairs of electrons on oxygen atoms, resulting in AIE. An attempt was made to examine if PEG 20K forms clusters in an aqueous solution using AFM and DLS.Although the GC–MS method employed here is qualitative in nature, ∼30 to 40% residual fluorescence intensity does not completely eliminate the possibility of PEG 20k having fluorescent properties. Particularly, previous studies reported the fluorescent properties of various PEG molecules.Figure S3). The topography of PEG 20k, deposited on the HOPG surface, was investigated by tapping-mode AFM. AFM images exhibited irregular-shaped aggregates . While the partial quenching occurred at various degrees for Na+, Mg2+ Co2+, and Cr3+, a complete quenching was observed for Cr6+ and Fe3+ for 3-BHA (Table S1). The partial quenching of 3-BHA was significantly more than what was observed for PEG 20k. These results, although not completely confirmatory, indicate that the origin of fluorescence could very well be the 3-BHA fluorophore trapped in the network of PEG clusters. However, the absence of any effect by Na+ and Mg2+ and a minor impact due to Co2+ and Cr3+ on the fluorescence intensity in PEG 20k increases the possibility of emission due to aggregation of the PEG matrix, which might involve delocalized electrons interacting “through space” within the PEG 20k aggregates.Controlled quenching experiments on 5 nM 3-BHA in water–ethanol (1:1 volume ratio) exhibited loss of fluorescent intensity across all metal ions  protons for 20 mg/mL PEG 20k solution. The NMR peak for the methylene protons shifted toward the low field with the increase in the PEG 20k concentration; the peak for methylene protons appeared at 3.622 ppm for PEG 20k at a concentration of 200 mg/mL  and those within a molecule (intramolecular interactions). After 200 ns simulation, each of the seven PEG 20k chains exhibited interactions, not only intramolecular but also between the PEG 20k molecules. Additionally, the structure became much more compact 12a. To iThe quantum chemical nature of the intramolecular interactions within the interior region of the folded PEG 20k was further investigated in small clusters of radii 5, 7.5, and 10 Å, with 70, 223, and 499 number of atoms, respectively 2. In add42 Furthermore, previous studies demonstrated that the M06-2X functional provides a reliable measure of energetics and geometry with a delocalized electron system.18The M06-2X functional was chosen for all calculations as this functional is parametrized to model the dispersion interactions that arise due to the electron correlational effect.n-to-Rydberg orbital transition.40 Our computation showed that as the size increased, the peak was red-shifted to 175 nm; this demonstrated more delocalization of the electron density  of these clusters appear to spread over a larger block of atoms, demonstrating increased delocalization 13. An in density 14 upon aThe analysis of partial charges of oxygen atoms with the increasing size of PEG 20k clusters portrayed a picture supporting the delocalization. As evident from the data provided in In contrast, for all clusters, a maximum partial charge for oxygen atoms of approximately −0.6 occurred 3. As theex ∼ 290 nm. Also, PEG 20k emission intensity increased with the increase in concentrations. In the aqueous state, the fluorescence intensity was maximum for PEG 20k compared to other PEG molecules when excited at 295 nm. The lack of fluorescence from lower molecular weight PEG molecules under the present experimental condition, which were earlier reported to exhibit fluorescence properties, prompted us to thoroughly investigate the origin of fluorescence in PEG 20k. We first checked the presence of any fluorescent molecules as impurities in the PEG 20k samples. The GC–MS experiments revealed the presence of 3-BHA in the commercially available PEG 20k. Removal of 3-BHA from PEG 20k using diethyl ether resulted in a significant decrease in the fluorescence intensity compared to the fluorescence intensity of the unpurified PEG 20k. This observation suggested that the emission observed between 300 and 400 nm from the aqueous solution of unpurified PEG 20K is mainly due to 3-BHA. Although the 3-BHA was not completely removed from the PEG 20k sample , the fluorescence experiment does not exclude the possibility of PEG 20k behaving as a non-traditional fluorophore.A detailed study, which involved experiments and computations, was conducted to investigate the origin of fluorescence in PEG 20k. Within the range of 280–300 nm excitation wavelength, the emission intensity of PEG 20k is maximum at ∼325 nm for λ2CH2−) toward the low field, from 3.599 to 3.622 ppm, suggesting the “through-space” delocalization due to cluster formation.Various experimental studies suggested that the PEG 20k could form clusters in aqueous solution via intermolecular and intramolecular interactions within and between PEG 20k chains. The formation of clusters, which favors electron delocalization, was observed by AFM imaging and size measurements through DLS. Also, the fluorescent anisotropy measurement revealed the existence of clusters of PEG 20k at higher concentrations at 25 °C; PEG 20k clusters dissociate at a higher temperature (50 °C). This cluster formation could favor the “through-space” delocalization of lone pairs of electrons and could make this non-traditional fluorophore fluoresce via “aggregation-induced emission”. However, the melting temperature experiment and effects of metal ions were inconclusive regarding AIE. Nonetheless, NMR analyses of 20 and 200 mg/mL demonstrated a chemical shift of the ethylene protons , which matched the reported UV–vis spectra of 3-BHA.43 This observation suggested that although PEG 20k forms clusters at higher concentrations, which favors “through-space” delocalization of lone pairs of electrons, there is no evidence that it absorbs in the 280–300 nm range. In contrast, 3-BHA is the main chromophore that absorbs at 290 nm and emits between 300 and 400 nm. Taken together, the observed absence of fluorescent properties of the smaller PEG molecules and the lack of absorption of the quantum chemically simulated PEG 20k core at the excitation wavelength used in earlier studies14 —despite “through-space” delocalization — strongly suggested that PEG molecules do not fluoresce between 300–400 nm . Furthermore, the presence of the fluorogenic 3-BHA in the commercially available PEG 20k could be deceptive regarding the fluorescence observed for PEG molecules, irrespective of their sizes. Therefore, the reported fluorescent properties14 of variable-sized PEG molecules should be re-examined.MD simulations of the PEG 20k monomer and heptamer in the gas phase and the aqueous phase also showed the existence of well-organized hexagonal columns in the folded polymers. These folded polymers were devoid of water molecules at the core, and the folding was favored due to hydrophobic interactions, intra- and intermolecular, between the methylene carbons. The hydrophobic clustering enabled electron delocalization among the organized oxygen atoms with lone pairs and is expected to induce the fluorescent property in PEG 20k. The electronic structure calculations of variable sizes of clusters, extracted from the core of a folded PEG 20k polymer, revealed ordered arrangements of hexagonal columns, where interatomic distances, i.e., C–C, O–O, and C–O, remained independent of cluster size. As the cluster size increased, electron clouds spread over a larger region, and the degree of delocalization increased. The electronic structure calculations provided a clear picture of the lone pairs of electron delocalization and the extended through-space delocalization. The computation of electronic transitions using TD-DFT revealed a single peak due to ter size 14. Howev


Cholera, an enteric disease caused by Vibrio cholera claims thousands of lives yearly. The disease is a disease of inequality that affect populations which have poor access to safe water and sanitation facilities. Zanzibar, an archipelago in the Indian ocean which is part of the United Republic of Tanzania has been affected by recurrent cholera outbreak for the past decades. A multi-sectoral and multi-year three pillar approach namely Enabling Environment, Prevention and Response, for the elimination of cholera were initiated by the stewardship of the government, engagement of the community and technical and financial support of partners. The approach has enabled Zanzibar to interrupt the recurrent cholera outbreak for the past five years. The analysis of evidences have proven that creating an enabling environment through multi-sectoral involvement, mobilizing communities, intensifying surveillance complemented by the traditional disease prevention and control interventions has resulted to interruption of cholera transmission in the country. Cholera is an acute enteric infection caused by Vibrio cholera through ingestion of the bacterium via contaminated water and food. It is estimated that cholera causes up to four million cases globally and 143,000 deaths yearly; 54% of these are reported from Africa . More thZanzibar has had recurrent outbreaks of cholera since 1978. Seventeen cholera outbreaks were recorded, with 14,364 cases and 210 deaths. The most recent major cholera outbreak lasted 10 months between September 2015 to July 2016, recording 4,330 cases and 68 deaths, with a case fatality rate of 1.6% [Essential medicines and supplies were pre-positioned in every district, and cholera beds were stored at the disaster management committee to be deployed whenever needed. In 2017, the World Health Organization (WHO) launched a roadmap for ending cholera by 2030, targeting to reduce cholera deaths by 90% and eliminate the disease from at least 20 endemic countries. The road map uses three strategies: early detection and quick response, targeted prevention, support resources, and partnership . AccordiWe conducted a retrospective analysis of the implementation of proven interventions as stipulated in the strategic document for 2018-2027 based on the three pillars identified for eliminating cholera in Zanzibar by 2027. Zanzibar, an archipelago of 46 islands, two of them being the major islands in terms of size and population, was the target for cholera elimination. The entire population of Zanzibar was targeted for cholera elimination with the implementation of specific interventions, such as the cholera vaccine for 322,483 people in cholera hotspot areas in both Unguja and Pemba.Data collection tool and analysis: policy and strategic documents, Integrated Disease Surveillance and Response (IDSR) data and Oral Cholera Vaccine (OCV) registries were monitored and analyzed. Post campaign coverage survey was conducted, and data was analyzed on Water, Sanitation and Hygiene (WASH) and vaccination coverage.Processes for program development for cholera elimination in Zanzibar: the highest office in Zanzibar, the president’s office, instructed all relevant sectors to work together and control the cholera outbreak of 2015-2016 and develop a multisectoral and multi-year cholera elimination plan. The instruction was communicated to key sectors that will play a major role in cholera control and elimination. The Ministry of Health was selected to be the technical arm of the initiative and requested WHO and other development partners for technical and financial support. A task force was formed composed of technical officers from MOH, Ministry of Water and Natural Resources, represented by Zanzibar Water Authority (ZAWA), Local Government/Municipality, UN agencies, and other development partners and NGOs. The task force had clear terms of reference and had regular meetings until the cholera elimination plan was drafted, a consensus-building meeting was done, and the Zanzibar Comprehensive Cholera Elimination Plan (ZACCEP) 2018-2027 was launched. The process was guided by the Second Vice President’s Office and MOH as a technical arm from the government side, and WHO as a coordinating agency for development partners.As indicated in the ZACCEP document, the implementation of activities and interventions was closely monitored through the coordination structures set out during the inception of the three pillars for eliminating cholera in Zanzibar .Enabling environment: the Revolutionary Government of Zanzibar has established a coordination mechanism for the cholera elimination agenda in Zanzibar. The coordination is led by the Second Vice President Office (SVPO). A high-level steering committee was established, composed of Principal Secretaries of all sectors and a task force that monitors the implementation of ZACCEP and reports to the steering committee for policy and strategic guidance. The steering committee was integrated into the existing Principal Secretaries coordination body that meets every week; however, it meets biannually for cholera and other emergency-related issues. The task force meets quarterly and on Ad-hock bases when required. This has been a very instrumental mechanism for multiple sectors and partners to work together to implement ZACCEP effectively , through the Urban Water and Sanitation Project, provided 40,944,216 US$ for water and sanitation infrastructure improvement in Zanzibar [Zanzibar . Under tChlorine tablets for household water treatment were distributed during community education and sensitization sessions. Three water reservoirs with more than 3 million liters capacity were built, and chlorination was done in these reservoirs before distribution for consumption. The water pipe system, which was old, and leaking, was changed in urban areas where recurrent cholera outbreaks were common. Two rounds of oral cholera vaccinations were conducted in July and August 2021 amid the COVID-19 pandemic targeting 322,483 people. A total of 295,849 (91.7%) individuals above 1 year of age were vaccinated in the first round and 188,354 (63.7%) in the second round. A total of 33 villages from nine districts were selected as cholera hotspots targeted for oral cholera vaccine and other public health measures, including water and sanitation improvement and social mobilization interventions. The target population was selected based on the intensity and recurrence of cholera outbreaks in the past , Table 1Response: to prepare for outbreaks, the Ministry of Health pre-positioned a set of intravenous fluids, oral rehydration solutions, chlorine tablets and antibiotics in every district for immediate response in case a cholera outbreak happens. The disaster risk management directorate at Vice President’s Office stocked tents, cholera beds, buckets for chlorine preparation and other amenities ready to be deployed in case of cholera outbreaks and other health emergencies. Because of the successful implementation of interventions across the three pillars of cholera elimination, namely Enabling Environment, Prevention, and Response, Zanzibar has not been reported since 2017. Significant progress has been made in Zanzibar since the cholera outbreak five years ago. The improvement of water and sanitation infrastructures, coupled with a comprehensive plan to eliminate cholera, has set Zanzibar on the path towards a future free from this disease and contributing to elimination of cholera in the United Republic of Tanzania (Tanzania . ApplicaThe three pillars approach for eliminating cholera is a prototype that Zanzibar adopted, including enabling environment, prevention and response. It was found effective in interrupting the recurrent cholera outbreak in Zanzibar. Other countries traditionally implement water, sanitation, and hygiene improvement, treatment of cases and oral cholera vaccines for interrupting cholera transmission in their respective countries. However, Zanzibar has added an enabling environment that invested in multisectoral coordination, implementation of rules and regulations related to food hygiene and sanitation and capacity building of sectors in responding to cholera outbreaks.Similarly, Zambia initiated a multisectoral action to control Lusaka’s cholera outbreak, coordinated by Zambia National Public Health Institute in 2018 . Haiti aWe also note that the OCV coverage in Cameroon, a reactive vaccination during COVID-19, had a similar coverage of 64.4% . A door-Using community volunteers and hygiene promoters to give lifesaving information on water and sanitation effectively reduced the spread of cholera in Yemen . DespiteImplementation of investment in water and sanitation is the primary and sustainable way of preventing cholera and other diarrheal diseases transmitted through contaminated food and water . The GovThe lessons learned from cholera can be replicated in controlling and eliminating other diseases of public health concern in the context of the One-Health approach. Continuous community sensitization, education and engagement and documenting their contributions towards cholera elimination are recommended to sustain gains and consolidate the elimination of the disease.We conclude that implementing the three pillars, namely enabling environment, prevention, and response, under the coordination of the Second Vice President’s Office has proven to be very effective in controlling and eliminating cholera in Zanzibar. Implementation of investment in water and sanitation is the primary and sustainable way of preventing cholera and other diarrheal diseases transmitted through contaminated food and water . The GovAlignment of the various strategic visions, missions and priorities is crucial for the success of strategic plans;Stakeholder engagement and participatory approach right from the inception of the concept in strategic plan to the finalization of the plan is critical in realization of goals and objectives as well as underlining accountability of each actor in the plan;The CCS has been a midterm strategic vision for WHO to support member states.The methods that we used in meticulous review of documents in the prioritization of interventions and focus areas, and the mapping of various strategic priority areas for alignment would help countries that will undergo similar process to follow. In addition, this helped in aligning the monitoring and evaluation framework of the CCS with the UNSDCF and the national health sector strategic plan.


Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning. Typically, UDA approaches assume the same categories in both domains. The effectiveness of transfer learning depends on the degree of similarity between the domains, which determines an efficient fine-tuning strategy. Furthermore, domain-specific tasks generally perform well when the feature distributions of the domains are similar. However, utilizing a trained source model directly in the target domain may not generalize effectively due to domain shift. Domain shift can be caused by intra-class variations, camera sensor variations, background variations, and geographical changes. To address these issues, we design an efficient unsupervised domain adaptation network for image classification and object detection that can learn transferable feature representations and reduce the domain shift problem in a unified network. We propose the guided transfer learning approach to select the layers for fine-tuning the model, which enhances feature transferability and utilizes the JS-Divergence to minimize the domain discrepancy between the domains. We evaluate our proposed approaches using multiple benchmark datasets. Our domain adaptive image classification approach achieves 93.2% accuracy on the Office-31 dataset and 75.3% accuracy on the Office-Home dataset. In addition, our domain adaptive object detection approach achieves 51.1% mAP on the Foggy Cityscapes dataset and 72.7% mAP on the Indian Vehicle dataset. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work. Experimental results also show that our work significantly outperforms the existing methods. Deep learning has achieved significant success in the field of computer vision in recent years, particularly in image classification and object detection using Convolutional Neural Networks (CNNs). Typically, CNNs are trained with supervised learning using large amounts of labeled data, drawn from an identical distribution for both training and testing the model. However, collecting and labeling data can be very time-consuming, labor-intensive, and expensive, especially for new tasks in various domains. In addition, adequate training samples do not always exist. Furthermore, the training of deep CNNs is domain-specific. The existing models show promising results on the dataset used for training. However, they often fail to generalize well to new, similar domains due to the problem of domain shift . Domain Domain adaptation is a type of transfer learning utilized to train a model with unseen data in the target domain by acquiring knowledge from a related source domain . The souAlthough deep transfer learning-based UDA approaches have seen a lot of success so far, they still face challenges that need to be overcome to improve their performance. The present study mainly focuses on aligning the marginal or conditional distributions and utilizing the pre-trained model for transferability. Transferability depends on the relatedness and size of the source dataset and target dataset. Transferability plays a significant role in fine-tuning the network to improve the performance of the target task; otherwise, negative transfer or overfitting may occur and degrade performance . In domaτ-score metric. The τ-score is obtained without training the deep network. Instead, we use the deep CNN for feature extraction only to calculate the τ-score, saving expensive computation time before the actual training of domain adaptation. Moreover, it is simple to understand and interpret and can be employed in various deep transfer learning CNNs. The τ-score has been utilized to identify the layer of the network to fine-tune, instead of selecting only the last few fully connected layers or randomly from the entire network. Secondly, we utilize the weighted cross-entropy loss for domain adaptive image classification and object detection to handle the class imbalance problem. Lastly, we employ the JS (Jensen–Shannon) divergence to calculate the domain discrepancy loss between the domains. The JS-divergence is symmetric and finite, and can be used to calculate the distance between the two distributions. Our domain adaptive object detection method is not dependent on the detection model, such as Faster R-CNN, as it does not consider the regional proposal loss to learn the domain invariant features. Our model only considers the image-level and object-level features to reduce the domain shift in object detection and can be implemented with a two-stage or one-stage object detection network.In this paper, our goal is to develop a unified unsupervised domain adaptation network for image classification and object detection that can learn more transferable feature representations and effectively reduce the domain shift problem. Firstly, we propose a guided transfer learning (GTL) approach to measure transferability between the source and target domains, called the The main contributions can be summarized as follows.To the best of our knowledge, we propose the first-of-its-kind layer selection strategy using the Guided Transfer Learning approach to fine-tune the domain adaptation network and maximize feature transfer between domains.We employ JS-Divergence to reduce the feature distribution gap between source and target domains.We introduce the weighted cross entropy loss to tackle the class imbalance problem.We further propose a robust object detection UDA framework that is applied to the two-stage Faster R-CNN and single-stage SSD (Single Shot multibox Detector) object detector effectively.We conduct extensive experiments on benchmark datasets to validate the performance of our UDA image classification and object detection method, compare it with state-of-the-art (SOTA) methods, and obtain promising results. Moreover, we also demonstrate ablation studies to show the impact of each component in our proposed framework. Furthermore, we present the first-of-its-kind Indian Vehicle dataset for domain adaptive object detection task to evaluate the adaptivity of our object detector in the new domain.The rest of the paper is organized as follows: The problem of transferring knowledge from a labeled source domain to an unlabeled target domain is said to be solved by unsupervised domain adaptation. Significant research contributions have been put into supervised and semi-supervised domain adaptation methods. In recent years, increasing research efforts are focused on unsupervised domain adaptation methods that use deep learning architectures to improve the performance of image classifiers and object detectors. To reduce the domain divergence between the source and target domain, there are mainly two main types of UDA methods that have gained significant attention: discrepancy-based UDA methods and adversarial-based UDA methods. In this section, we describe recent works on these approaches for domain adaptive image classification and object detection.In discrepancy-based methods, domain adaptation is achieved by minimizing the distance between domain distributions using statistical measures to find domain invariance features.Ghifary et al.  introducAdversarial-based methods train discriminator networks to confuse the domain distributions. The domain-adversarial neural network (DANN) was first introduced in  for use In past decades, CNN-based object detection methods have shown significant improvements applied to various datasets and have been successfully utilized in many computer vision applications. Object detection algorithms are categorized into two-stage ,41,42 anChen et al.  proposed2net method with the object-aware alignment (OAA) and optimal transport-based alignment (OTA) modules to apply pixel and instance levels domain alignment loss. Zhu et al.  aligned This section presents the proposed algorithm (Domain Adaptation using Guided Transfer Learning—DAGTL) in detail. First, the notations are defined for domain adaptation, and the problem statement is formulated. Then, we introduce the guided transfer learning approach to select the layer from which the model is fine-tuned. Next, we explain two proposed approaches: Domain Adaptation using Guided Transfer Learning for image classification (DAGTL-IC) and Domain Adaptation using Guided Transfer Learning for object detection (DAGTL-OD). Finally, we present the overall objective functions to minimize the loss for domain adaptive image classification and object detection.x denote the input image and y denote the corresponding label of the image. We define the domain as D = {X, P(x)}, where X is the feature space 1, x2, …, xn}X = {x and P(x) is a marginal probability distribution. The proposed algorithm is designed to address the problem of unsupervised domain adaptation and aims to adapt the features from a label-rich source domain Let The deep domain adaptation utilizes the deep convolutional neural network to improve the performance of the target task for image classification and object detection. Our proposed work is based on the discrepancy-based deep domain adaptation approach. In this approach, transfer learning is performed by fine-tuning the convolutional neural network using target data to minimize the distribution shift between the source and target domains.The convolutional neural network requires a large amount of labeled data to train the model from scratch. However, this assumption does not necessarily hold in real life due to the scarcity of annotated data. Additionally, deep learning algorithms are data-dependent for a particular domain or problem. These algorithms require re-training when the domain shifts. The transfer learning approach utilizes the knowledge from the source domain to improve the performance of the target task in the related target domain. Therefore, transfer learning substantially enhances the model performance and decreases the model development time. However, the effectiveness of transfer learning depends on the size of the target domain and the similarity between domains. In domain adaptation, source, and target domains share common label space but feature distributions are different and the size of target data is also small. Therefore, we applied the fine-tuning strategy of transfer learning on the target domain using deep convolutional neural networks. The convolutional neural networks are designed with a hierarchical representation of layers, including convolutional, pooling, and fully connected layers. The initial layers of CNNs learn simple features of an object, while higher-level layers attempt to learn more complex and abstract features. In the fine-tuning strategy of transfer learning, initial layers are frozen to reuse low-level features, and deeper layers are fine-tuned to train the parameters based on the relatedness of the source and target domain. The transfer learning algorithm trains partial layers of the pre-trained network. Selecting partial layers of pre-trained CNN models depends on the correlation between the target and source domain. Therefore, it is essential to investigate the effective layer selection strategy from which layer to freeze and fine-tune the model to perform the target task efficiently, rather than selecting layers empirically or randomly.The proposed guided transfer learning approach is illustrated in kth layer (Let Algorithm 1: Guided Transfer Learning (GTL) approach to find the kth layerInput: labeled source domain Output: kth layer (1:Take five random samples  from the Source domain ) and Target domain ) for each label 2:Pass all samples through the ResNet-50 network up to the last convolution layer to generate a flattened feature vector and apply the mean on five feature vectors of the same labeled images. j = 1 to 5 (#samples), i = 1 to 3:Apply JS (Jensen–Shannon) distance among  source feature vectors and  the target feature vector of each label to find the similarity score  between each source label to the target label. Transferability measures should be symmetric in the domain adaptation because the label space is common in both domains and the distance between two feature vectors is computed using JS-Divergence. The JS-Divergence can be calculated as follows between the two distributions P and Q: 4:Calculate Transferability Score . 5:kth layer.Find the  Layers In this paper, we introduce the discrepancy-based unsupervised domain adaptation framework using GTL for image classification (DAGTL-IC) and object detection (DAGTL-OD). In unsupervised domain adaptation, the target domain cannot be directly used to fine-tune the model for the target task due to unlabeled data. In the proposed approach, we utilize labeled source data and unlabeled target data with the same categories of class. The objective is to reduce the domain distribution distance using domain loss functions and to learn better transferable feature representations, which can improve the overall performance of the target task. This is achieved by fine-tuning the ResNet-50 network for image classification and fine-tuning the Faster R-CNN and SSD for object detection. The ResNet-50 network is used as the backbone to extract features in object detection models. The ResNet-50 model comprises five stages, each stage contains a convolution and Identity block. Each convolution block and identity block have three convolution layers. There are forty-nine convolutional layers and one fully connected layer. A detailed explanation of the proposed approaches is given in subsequent sections.The proposed DAGTL-IC architecture consists of two streams of ResNet-50 networks for the source and target domains with weight parameter sharing, as illustrated in Classification lossClassification loss is calculated using the first stream of the architecture. It uses the labeled data from the source domain only as the target domain does not have labeled data. The features extracted from the last flattened layers are fed into the output layer with a softmax classifier to optimize the classification loss. The classification loss function can be written as follows:The classifier is expected to train well the conditional probability of input data Let ith category.F, i.e., the highest number of samples among all categories.ith category, which represents how important that category is in the dataset.The formula calculates The intuition behind this equation is to assign lower weights to categories that appear more frequently, and higher weights to categories that occur less frequently. This equation balances the importance of each category during training and reduces the class bias problem due to an imbalanced dataset.Dyomain discrepancy lossThe domain discrepancy loss is computed between the bottleneck layers of both domain streams. To minimize the distance between the domains, JS-Distance is employed to learn the domain invariant features. We intend to transfer as much knowledge as possible from the source domain to the target domain by minimizing the domain alignment loss. The feature vectors of the bottleneck layers of the source and target domains are denoted as JS-Distance is the square root of the JS-Divergence and its value ranges between 0 (highly similar distributions) and 1  when using a base-2 logarithm. JS-Divergence is a method used to measure the similarity between two probability distributions. The reasons to use JS-Divergence are two folds. These are: (i) It is a symmetric version of KL-Divergence and can be used to calculate the distance between distributions because it has a finite range between 0 and 1. (ii) It is a kind of average between two distributions, thus two distributions are equally participating to find the domain invariant features.Overall objective loss function for domain adaptive image classificationTo achieve efficient domain adaptation in image classification, the aim is to minimize the distance between the domains and train a classifier that can be transferred across the domains. To meet both these criteria, an integrated approach is used by combining the classification loss and domain shift loss as an overall objective loss function with a trade-off parameter. The objective is to minimize the overall loss. After reducing the overall loss to a minimum, the trained model is directly applied to the target domain. The overall objective loss function for image classification is given as follows:The overall objective loss function of DAGTL-IC:λ is the trade-off parameter; Algorithm 2: Unsupervised Domain Adaptation for Image Classification (DAGTL-IC)Input: labeled Source domain λ. Output: Domain invariant features C.1:Configure the CNN. Initialize the ResNet-50 model until the last convolutional layer, then add a bottleneck layer of 512 neurons. Finally, add an output (classification) layer with a number of neurons that is equal to the number of categories in the dataset.2:kth layer Find the 3:Freeze the layers 4:Repeat5:Sample mini-batch from the source domain with labeled data and the target domain with unlabeled data6:Feed the sampled mini-batch and calculate domain discrepancy loss 7:Update the parameters of the network by minimizing the overall loss 8:Until 9:Return trained Classifier The proposed DAGTL-OD architecture consists of two streams of the object detection network for source and target domains as illustrated in Detection lossThe object detection model is trained with classification loss and regression loss. Classification loss Domain discrepancy lossIn object detection, there are two important aspects for reducing the shift between the domains: whole image differences like scale, illumination, etc., and particular objects of the image differences like scale, and appearance. To align the distribution between domains, we introduce the two types of losses in the proposed network training: image-level domain discrepancy loss jth ROI vector in the ith image as follows.The object-level features are obtained from the vectors of the region of interest. These feature vectors from both domains are utilized to compute Overall objective loss function for domain adaptive object detectionTo obtain an effective domain adaptive object detector, we attempt to reduce the domain shift gap across the domains including the classification and regression loss of the object detection model. We combine the detection loss and domain shift loss as an overall objective loss function with a trade-off parameter, and our goal is to minimize the total loss. After reducing the overall loss to a minimum, the trained detector model is directly applied to the target domain. The overall objective loss function for object detection is as follows.The overall objective loss function of DAGTL-OD:Algorithm 3: Unsupervised Domain Adaptation for object detection (DAGTL-OD)Input: labeled Source domain λ. Output: Domain invariant features at image-level 1:Configure the object detection model. Initialize the backbone network as a ResNet-50 model until the last convolutional layer. Add detection head (Faster R-CNN/SSD).2:kth layer Find the 3:4:Repeat5:Sample mini-batch from the source domain with labeled data and the target domain with unlabeled data6:Feed the sampled mini-batch and calculate object detection loss 7:Update the parameters of the network by minimizing the overall loss 8:Until 9:Return trained detection network In this section, the proposed DAGTL-IC and DAGTL-OD have been extensively evaluated on four benchmark domain adaptation datasets. Firstly, dataset description and implementation setup along with hyper-parameter value are described. Secondly, the DAGTL approaches are compared with the state-of-the-art domain adaptation methods to examine their performance. Lastly, we analyze the performance by taking various trade-off parameter values and present an ablation study to know the impact of each component on the overall performance of the proposed DAGTL algorithms.Office-31  is a widOffice-Home  is a staCityscapes  is a datFoggy Cityscapes  is a synThe Indian Vehicle dataset is collected from the CCTV cameras of six different crossroads in Vadodara city, Gujarat, India. This dataset contains 3500 images of real road scenes with five categories of objects. This dataset is divided into 2975 images for the training set and 525 images for the testing set. Object categories are car, truck, bus, motorcycle (bike), and bicycle with rectangle annotations. Each image is a color image, and the resolution is 1359 × 720. This dataset is utilized to show the effect of adapting to a new dataset. Samples are illustrated in The proposed DAGTL methods are implemented using the PyTorch deep learning library. The hardware configuration of the system was 64 GB RAM, Nvidia RTX A4000 (16 GB) graphics card, and an Intel Xeon processor. The ResNet-50 architecture has been selected as the backbone network for the proposed DAGTL approaches for two reasons. Firstly, ResNet-50 has a relatively smaller number of parameters compared to other deeper convolutional neural network (CNN) architectures, which makes it computationally efficient, less prone to overfitting, and easier to fine-tune for domain adaptation tasks. Secondly, other existing methods have also utilized ResNet-50 as the backbone network. Therefore, employing ResNet-50 enables a fair comparison of the proposed DAGTL approaches with other state-of-the-art methods. The proposed model is fine-tuned to update the weights from –4. The base learning rate is 0.0001, which is used in frozen layers, and the learning rate is set to be 10 times higher than the base learning rate for the layers trained from scratch. Five random experiments are conducted for all the transfer tasks of Office-31 and Office-Home datasets, and the average result in accuracy is reported. A trade-off parameter (λ) of 0.8 was chosen from {0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 1, 1.5, 2}, which balances the classification loss and discriminative losses to achieve minimum objective loss value.We follow the standard evaluation protocol for our UDA transfer tasks, which is considered in ,13,23 toλ = 0.2 and 0.4 for the Cityscapes → Foggy-Cityscapes (C → F) and Cityscapes → Indian Vehicle Dataset (C → I) domain transfer tasks, respectively. The foggy dataset has three variants of fog intensity levels, and we choose the highest intensity level of fog (β = 0.02) as per the baseline algorithms for fair comparison for the C → F domain transfer task.We utilize the Faster R-CNN and SSD models as object detection networks with ResNet-50 as the backbone network. We consider a 0.5 IoU threshold for experiments to evaluate mean average precisions (mAP). Training and testing set images are resized to 600 pixels in length. The batch size is set to 16, and the learning rate is 0.001 for 60 epochs and 0.0001 for the remaining 40 epochs. We use stochastic gradient descent (SGD) to train the model with a momentum of 0.9 and a weight decay of 0.0005. We set We evaluate the effectiveness of our proposed DAGTL methods by comparing them with various state-of-the-art deep domain adaptation algorithms, which are discussed in the related work and presented in 2net [2net. It is worth noting that the performance of our model in each category is highest except for the rider when Faster R-CNN is the base model. This indicates that the DAGTL-OD approach can decrease the domain gap across various objects. Detection samples are shown in Weather is a significant cause of domain discrepancy, as changing weather conditions result in a visibly distinct scene. We utilize the Cityscapes and Foggy Cityscapes datasets as the source and target domains, respectively, to assess the model adaptability from normal to foggy weather conditions. 2net  by 2.9%.Domain adaptation is essential when changes occur in intra-class variations, camera sensors, geographic areas, or environmental setups. We investigate the adaptability of our method for such changes by considering the Cityscapes dataset as the source domain and the Indian Vehicles dataset as the target domain. To the best of our knowledge, this transfer task (Cityscapes → Indian Vehicles) is the first of its kind. Both are real datasets and we consider five categories including car, truck, bus, motorcycle, and bicycle from both datasets for our experiments. To verify the feature transferability of the proposed DAGTL approaches, we utilize t-Distributed Stochastic Neighbor Embedding (t-SNE)  to visuaλ on classification tasks A → W and A → D and object detection tasks C → F and C → I. λ ∈ {0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 1, 1.5, 2} for classification and λ ∈ {0.1, 0.2, 0.4, 0.6, 0.8, 1} for object detection. The trade-off parameter λ balances the contributions of domain discriminative loss in the overall objective function which leads to obtaining more discriminating features. λ = 0.8 for classification and λ = 0.2 and 0.4 for object detection, then accuracy decreases as λ increases, following a bell-shaped curve. This demonstrates the effectiveness of λ in joint training of learning the classification task and discriminative features for domain adaptation which improves the feature transferability.We investigate the sensitivity of the parameter In this section, ablation experiments are demonstrated to investigate the impact of individual components of the proposed model on overall performance. DAGTL-IC consists of three components with the ResNet-50 network: domain adaptation loss, GTL strategy, and weighted classification loss. We conduct a comprehensive evaluation of DAGTL-IC on a subset of tasks from the Office-31 and Office-Home datasets, and In this paper, we propose a novel unified unsupervised domain adaptation network to tackle feature transferability during fine-tuning and to align the source and target domain distributions simultaneously for image classification and object detection tasks. We introduce the layer selection strategy using the guided transfer learning approach to fine-tune the model for better knowledge transfer between source and target domains. Furthermore, we employ the JS-Divergence to reduce the domain discrepancy between the domains, which can obtain the domain-invariant features and align the domain distribution. Our proposed UDA networks utilize the ResNet-50 network as a backbone. Extensive experimental analysis reveals that our proposed method has the ability to learn the domain invariant feature representations by training the algorithm using the layer selection strategy and domain discrepancy loss. It is also observed in the ablation study that our method has an obvious advantage in learning more transferable features based on the similarity score between the domains using the layer selection strategy. It is also important to note that training of our objective function is based on domain discrepancy loss, thus it requires less convergence time compared to adversarial-based approaches. The DAGTL-IC approach improves accuracy by 1.8% and 2.6% on Office-31 and Office-Home datasets, respectively compared to the state-of-the-art method. Similarly, the DAGTL-OD approach achieves 4.3% and 14.7% improvements in mAP on Foggy Cityscapes and the Indian vehicle dataset, respectively. These results demonstrate the effectiveness of our approach for domain adaptive image classification and object detection.In the future, the proposed approaches can be applied to various real-world applications where improving performance through transferability and domain alignment between source and target domains are primary concerns. Furthermore, these approaches can be extended to other deep CNN backbone networks to enhance the performance of image classification and real-time object detection.