Release of ortho-nitrophenol following the hydrolytic cleavage of ONPG by lactase is measured by a change in absorbance at 420 nm, and the effect of the temperature on the enzymatic reaction is evaluated by carrying out the reaction on ice, at room temperature and at 37 °C. More advanced analysis can be implemented using this protocol by assessing the enzyme activity under different conditions and using different reagents.Understanding how enzymes work, and relating this to real life examples, is critical to a wide range of undergraduate degrees in the biological and biomedical sciences. This easy to follow protocol was developed for first year undergraduate pharmacy students and provides an entry-level introduction to enzyme reactions and analytical procedures for enzyme analysis. The enzyme of choice is lactase, as this represents an example of a commercially available enzyme relevant to human disease/pharmaceutical practice. Lactase is extracted from dietary supplement tablets, and assessed using a colorimetric assay based upon hydrolysis of an artificial substrate for lactase (ortho-nitrophenol- Enzymes are a specialized type of protein that act as biological catalysts for chemical reactions in living organismsEscherichia coli through to Homo sapiens Figure 2)via the citric acid cycle and oxidative phosphorylationLCT) coding for the lactase enzymeThe glycosidic hydrolase lactase (EC 3.2.1.23/26) is an enzyme of central nutritional importance for mammalsDuring childhood, lactase expression is normally reduced; however, this reduction following weaning varies geographically, with approximately 35% of adults worldwide continuing to express the enzymeLCT gene expression is modulated by polymorphisms located in introns of the nearby MCM6 gene. Individuals with sustained expression of lactase carry polymorphisms that function as strong distal enhancers for LCT gene expression, thus compensating the normal down regulation of LCT transcription during weaning, and consequently sustaining lactase expression in adulthood9The control of The symptoms resulting from ATH can be managed by reducing lactose intake, for example by removing dairy products from the diet. An alternative approach for ATH, and the approach of choice for CLD, is the use of lactase supplements, widely available from pharmacies. These supplements provide lactase isolated from a variety of sources, including yeast and bacteria, in a liquid or pill-based form that can be taken with or added to lactose containing food. The supplement will hydrolyze a proportion of the lactose present in the food to glucose and galactose products, thus permitting their absorption and preventing accumulation of undigested lactose substrate in the gut.beta-D-galactopyranoside (ONPG) to provide a colorimetric end point for measuring cleavage of glycosidic bonds by lactase Based upon the use of lactase supplements as a dietary aid, we have developed a simple enzymology laboratory experiment suitable for first year biomedical science or pharmacy students. This laboratory experiment takes advantage of commercially available lactase supplements, and uses ortho-nitrophenol-NOTE: The protocol below was developed to be held over the course of two hours (including completion of in-class worksheets) in a purpose-built teaching laboratory. The experimental steps were deliberately put together to exclude more detailed analysis of enzyme kinetics (carried out in the context of this course using model data); however — as noted in the discussion — the protocol provides a template for more advanced analyses depending upon time, facilities and student attainment level.SAFETY: This practical should be carried out according to the rules of Good Chemical Laboratory Practice (GCLP) — personal protective equipment, including fastened laboratory coat, disposable gloves and safety glasses should be worn at all times in the laboratory.Crush one lactase tablet, containing 200 mg of enzyme, to an even powder using a mortar and pestle.Place the resulting powder into a 15 mL tube labelled "suspension" and resuspend in 10 mL of 100 mM phosphate buffered saline (PBS).Vortex for 1 min to maximize enzyme extraction.g to sediment solid particles.Transfer 1 mL from the suspension into a 1.5 mL centrifuge tube and centrifuge for 1 min at 10,000 x Transfer 500 µL of the supernatant to a clean 1.5 mL tube labelled "Lactase Extract".Place 390 µL of 100 mM PBS into a 1.5 mL tube labelled "Reaction A".beta-D-galactopyranoside (ONPG) as a substitute for lactose. Since ONPG is a phenolic compound, it should be handled with caution. In the event of skin contact with ONPG, the exposed skin should be washed immediately. Any spills should be wiped up immediately with paper towels to be disposed of into the appropriate waste stream.Add 100 µL of 5 mM ONPG solution and mix well by vortexing. CAUTION: This practical uses ortho-Nitrophenol-Add 10 µL of extract into the reaction A tube and mix well by vortexing.Observe the reaction mixture for 5 minutes and note any colorimetric changes to the solution.Set up two 1.5 mL tubes: one labelled "Reaction B", one labelled "Control".Add 390 µL of 100 mM PBS to the reaction B tube, 400 µL of 100 mM PBS to the control tube and then add 100 µL of 5 mM ONPG to each tube.Mix the contents by vortexing.Add 10 µL of lactase extract to the reaction B tube, mix by vortexing and allow the reaction to proceed for 1 min at room temperature.Once 1 min has elapsed, add 500 µL of 1 M sodium carbonate to both tubes to inhibit the lactase enzyme by increasing the pH, thereby terminating the reaction.Transfer 500 µL from each tube into clean spectrophotometer cuvettes and measure absorbance at 420 nm using the spectrophotometer.Note absorbance for reaction B and control samples, and then subtract the control value from the reaction value to derive the change in absorbance due to hydrolysis of ONPG in the presence of active enzyme."4 °C", "Room Temp", and "37 °C".Set up three control tubes and three reaction tubes as described in section 3 and label theseFollowing addition of enzyme extract, incubate one tube on ice, one at room temperature, and one at 37 °C (in a pre-heated water bath).Allow the reactions to proceed for 1 min and then terminate the reaction by adding 500 µL of 1 M sodium carbonate to each tube.Measure the absorbance at 420 nm for each tube as described in section 3. Record the values, subtracting the control value from the reaction value in each case.ortho-nitrophenol. Representative results for Section 2 of the protocol are shown in : E. coli beta-galactosidase. (A) Crystal structure of beta-galactosidase from Escherichia coli, showing the tetrameric structure of the active complex. (B) Allolactose (shown in red) bound to the active site of beta-galactosidase. Images generated from PDB 1JZ8Please click here to view a larger version of this figure.:Enzymatic action of lactase. Hydrolysis of lactose by lactase to produce beta-D-galactose and beta-D-glucose. Please click here to view a larger version of this figure.: Hydrolysis of ortho-nitrophenol-beta-D-galactopyranoside. ONPG hydrolysis by lactase to produce beta-D-galactose and ortho-nitrophenol (which is yellow in color), allowing estimation of lactase activity by measurement of absorbance at 420 nm. Please click here to view a larger version of this figure.:ONPG hydrolysis. Representative results of ONPG hydrolysis, showing control and reaction tubes (A) and representative data from Section 4 of the protocol recorded on in-class worksheets, showing raw absorbance in arbitrary units at 420 nm, corrected for background and at three different temperatures (B). Please click here to view a larger version of this figure.Table 1: Potential extended experimental condition. Suggested extended experiments, to be carried out in triplicate, investigating specific aspects of lactase biology.A detailed knowledge and understanding of enzymes, enzymatic reactions and enzyme kinetics is required for a wide range of topics spanning biology, biomedicine and pharmacology. The protocol described above uses lactase as an example of an enzyme relevant to human health for demonstrating enzymatic reactions, providing key skills that can be used in more advanced laboratory-based experiments.This protocol has been deliberately designed to be as robust as possible, allowing students with little or no wet laboratory experience to produce interpretable results in a short space of time. In the 2014/2015 academic year at the University of Reading School of Pharmacy, a total of 144 students, organized into groups of 3, carried out the "measuring lactase" experiment, with all groups able to detect some level of enzymatic activity from lactase tablet extracts.There are a number of critical steps in this protocol. First, it is essential that the initial extraction is efficient, allowing solubilization of enough active enzyme for subsequent analyses. In our experience, first year undergraduates can achieve this by following the protocol as outlined; however some guidance from laboratory demonstrators was required at this point. Although it would seem a self-evident point, a key factor in the success of this protocol is the ability to carefully measure volumes, correctly label tubes and follow instructions. While a certain level of proficiency can be assumed for many students, careful monitoring and a clear request for students to ask for assistance in the event that they are unsure of what to do next provides the greatest likelihood of a successful assay.Student evaluation can be carried out by the in-class worksheet , asking students to note data from the experiments, comment on their results and link to background information/reading from lecture material, or by more detailed, out of class assessment with model data provided allowing for kinetic analyses to be attempted and evaluated.Table 1). A detailed protocol for kinetic analysis of lactase has previously been publishedThe protocol presented herein is a simple example of a lactase activity assay under teaching laboratory conditions, and there is ample opportunity to increase the complexity of the experiment to allow more advanced analyses. In particular, the presented protocol provides an introduction to the concept of enzyme kinetics but does not allow for kinetic analysis of experimental data. As such, we would recommend viewing the described protocol as an initial template for classes, with the precise details adapted to suit the specific aims and talents of the student cohort undertaking the experiments. Examples of extended experiments could include: repeated measurements at different temperatures, using different substrate concentrations to allow statistical analysis, kinetic analysis of data (suitable for biochemistry students/majors), starting with several different tablets and allowing the students to evaluate different activity of enzyme in each , assessing the impact of heat denaturation, or carrying out competition experiments by addition of lactose or inhibitors of lactase (An important strength of this protocol is that it combines a basic introduction to enzymology and enzyme kinetics with a real-life case study of enzymology in medicine. This makes this laboratory experiment of particular relevance to students of medicine, pharmacy, biomedical sciences and related subjects. Importantly, the fact that there is both a serious medical condition and a more widespread dietary issue associated with lactose catabolism provides the opportunity to raise a wide range of medical and ethical issues with the students. This could include the issues surrounding genetic testing for medical conditions, and associated discussions relating to gene therapy and genetic counselling, as well as the use and sale of medicinal supplements. One major limitation is that the protocol does not allow for precise estimation of enzyme concentration, due to the starting material used for the extraction. A modification to account for this, however, would be to use reagent grade enzyme for the assay. Importantly, this would also allow for more precise calculation of kinetics for the lactase enzyme.In conclusion, assaying the activity of lactase in a teaching lab environment provides a robust, engaging and interesting introduction to the field of enzyme biology for early stage university students.The authors have no conflicts of interest to disclose.


Temporal aspects of eating are an integral part of healthy eating, and regular eating has been associated with good diet quality and more successful weight control. Unfortunately, irregular eating is becoming more common. Self-monitoring of behavior has been found to be an efficient behavioral change technique, but the solution should be simple enough to ensure long-lasting adherence.This study aimed to explore the influence of self-monitoring of daily eating pattern with mobile phone app on eating rhythm, eating behavior tendencies, and the underlying motives and attitudes related to eating.Button, was developed for effortless self-monitoring of eating rhythm. The feasibility of the app was tested in a 30-day intervention. The participants (N=74) recorded their eating occasions during the intervention by pressing a button in the app widget.A mobile phone app, The average interval between meals increased (96 [SD 24] min during the first 10 days vs 109.1[SD 36.4] during the last 10 days) and the number of daily eating occasions decreased (4.9 [SD 0.9] during the first 10 days vs 4.4 [SD 0.9] during the last 10 days). The tendencies for cognitive restraint, emotional eating, and uncontrolled eating increased. Eating-related attitudes and motives remained largely unchanged.These results indicate that a simple self-monitoring tool is able to draw a user’s attention to eating and is a potential tool to aid people to change their eating rhythm. Eating rhythm is an integral part of healthy eating. A regular eating pattern including breakfast, lunch, dinner, and 1 to 2 snacks has been found to be associated with good diet quality . In addiunsynchronized eating patterns) have increased in Nordic countries; approximately one-fifth of Danish, Finnish, Norwegian, and Swedish people have been found to possess unsynchronized eating patterns during weekdays and about one-third during weekends [grazing pattern in which there are no clear meal times but frequent peaks of eating occasions during the day [Reports from different parts of the world suggest that irregular eating is becoming more common. Irregular eating patterns that manifest as a tendency to skip conventional meals  was developed for the self-monitoring of eating rhythm in real time. At present, there are apps available  reminding about eating times  or coaching fasting  for certain time periods. Unlike the current apps, the Button app does not remind or coach the user to eat but makes eating rhythm visible and thus grants the ownership of eating rhythm to the user. The idea of the app is to offer the user a simple tool to become aware of his or her temporal eating pattern, which might act as a stimulus to regularize eating.The EMA tool  is more influential.The Button comprises 2 components: the desktop widget and the actual app. The user presses 1 of the 2 buttons reflecting different types of eating occasions  in the Button widget after each eating occasion to record the time stamp and type of the eating occasion. The Button app visualizes the user’s eating pattern with 3 summary screens . The datThe app development was an iterative process including 2 real-life user trials in January 2017 and April 2017. The aim of the user trials was to find a feasible way to record the eating occasions to guarantee the technical functionality of the Button app and easiness to use. Volunteers  used the app for a 2-week period, which was followed by a focus group discussion about the usability aspects and recommendations for further development. The participants of the first trial found it nebulous to interpret the graphs related to the optimal interval between meals and number of eating occasions per day. Therefore, green horizontal lines depicting the recommendations  were included in the app after the first trial. This change increased the easiness of the data interpretation in the second trial.Healthiness version and Contentment version) were developed. The Healthiness version had green and red buttons, the green button meaning an eating occasion that the user perceived as healthy and the red button meaning an occasion that was perceived as unhealthy. The Contentment version had green and yellow buttons. The green button reflected an eating occasion that the user was content with and the yellow button reflected an eating occasion the user was not fully content with. Only the meanings for the 2 buttons of the widget varied, but the functionalities of the 2 app versions were the same.The user trial participants found color coding in the Button widget as the most feasible option to differentiate eating occasions. They also preferred the healthy (green)-unhealthy (red) dichotomy for the buttons. Considering that healthiness is not the only viewpoint when evaluating eating and food choices, a version with content (green)-discontent (yellow) dichotomy was also developed. It was considered that the possibility to make a subjective judgment about whether the eating episode is subjectively good or bad versus normative healthy-unhealthy logic might provide the user with a stronger feeling of autonomy and commitment and therefore motivate the user to change eating habits -25. ThusParticipants (n=74) were recruited through public advertisements, email advertisements, and institutions’ intranets in 2 university campus areas in Finland. The participants had to be over 18 years of age and interested in well-being. In addition, an updated Android phone (Version 4.3 or newer) was a prerequisite for the attendance. The volunteers with red-green color blindness (self-reported) were excluded from the study.The eligible volunteers were invited to the study location where the details of the study were explained, and they had a chance to ask questions. After receiving both written and verbal information about the study , the volunteers signed an informed consent form. The Button app was installed in the participants’ personal mobile phones, and they were instructed on how to use the app. The participants were given 4 movie tickets worth 52 euros to compensate their time and effort. Data collection was conducted in May and June 2017. The study protocol was approved by the Coordinating Research Ethics Committee of the Helsinki and Uusimaa Hospital District. The study was conducted according to the ethical principles of good research and clinical practice described in the Declaration of Helsinki.Healthiness group or Contentment group. Background information about the participants is given in The participants were randomly distributed to Healthiness group and Contentment group, The study adopted within- and between-subject design with 2 independent study groups using 1 of the 2 app versions  or green or yellow (Contentment group) buttons after eating according to their own evaluation about the eating occasion. No instructions were given about eating rhythm or dietary choices. The participants were also instructed that they could freely open the Button app and observe the visual summaries of their eating rhythm and add comments. The intervention lasted for 30 days. Surveys measuring participants’ self-reported daily meals, attitudinal constructs, food choice motives, and eating behavior tendencies were administered before and after the intervention period to detect potential changes caused by the Button usage.The participants were instructed to record the eating occasions  by pressing either green or red  who completed the 30-day intervention successfully. Those participants (n=15) who had more than 4 days per 1 of the 3 10-day periods without any stamps because of technical difficulties or incompliance were removed from the Button data. The analytical approach was chosen as the Button app is intended to be used voluntarily and thus those participants who continued to use the app during the entire study were deemed to represent the real-life user. Pre and postsurvey data were analyzed on an intention-to-treat basis including all the 74 participants in the analyses. A different analytical approach was applied as all users had been exposed to Button usage at least to some extent during the entire 30-day period. Therefore, the survey data from the participants with and without valid Button data were deemed comparable.The collected Button data were preprocessed to screen out faulty data and to prepare them for the actual analysis. Records deemed as duplicate values (more than 1 timestamp within 5 min) were removed, and timestamp values were converted from server time to actual local time. To determine the actual number of eating occasions per day, it was decided that the day begins and ends at 4 am instead of midnight, and the data were handled accordingly. Before the analysis, data were treated in the following manner: (1) for the evaluation of the changes in eating rhythm, the 30-day intervention period was divided into 3 10-day periods, (2) all days with less than an average of 1,000 seconds (<17 min) interval between the meals were removed  as this was considered as an indication of multiple miss-presses or a technical flaw; moreover, evident outlier days with only 1 or 2 stamps per day were removed, (3) after suspicious data were removed, an average was calculated for each participant for each 10-day period, and the average was applied for the days with missing data.SPSS Statistics  was used for the statistical analysis. Repeated measures analysis of variance (ANOVA) with Bonferroni adjustment for multiple comparisons was used to analyze the within-group changes in eating rhythm  and adherence . A 1-way ANOVA was carried out to study between-group differences in data derived from the Button app . False discovery rate (FDR) was controlled by using the Benjamini-Hochberg method. The survey data analyses were conducted for all the participants (N=74) and separately for the subgroups. Repeated-measure ANOVA with Bonferroni adjustment for multiple comparisons were conducted to analyze within-group changes between presurvey and postsurvey. A 1-way ANOVA was used to analyze between-group differences in the self-reported number of daily meals, eating behavior tendencies (TFEQ-R15), discontent with eating habits, general health interest, and food-related motives. Benjamini-Hochberg method was used to control the FDR.Responses to the open-ended question about insights gained during the intervention period were analyzed following the standard content analysis procedures. Reported individual insights were categorized on the basis of their content in appropriate higher-order subcategories and finally in main categories.F1,694=6.241, P=.003) during the last 10 days of the intervention (P=.03). The average interval between eating occasions did not vary in the Contentment group. Between-group analyses revealed no statistically significant differences in any of the periods .The average interval between eating occasions (on the basis of button presses) was 96±24 min in all participants during the first 10 days of intervention, and it increased to 109.1±36.4 min . The number of the reported daily meals was significantly lower after the intervention in all participants  and again mainly caused by the change in Healthiness group. There were no statistically significant differences in the number of daily meals between the groups after the intervention .In the presurvey, participants reported to consume approximately 4.5±0.9 meals per day . There wF1=.333, P=.57; Period 2: F1=.289, P=.59; and Period 3: F1=.007, P=.93). On average, 77% of the eating occasions of the Healthiness group were classified as healthy and 86% of the eating occasions of the Contentment group were classified as content.The number of reported eating occasions (button presses) reduced during the 4-week intervention among all participants . The treF1=2.042, P=.16; Period 2: F1=1.126, P=.29; and Period 3: F1=1.207, P=.28).Adherence to use the Button app decreased during the study among all participants. They opened the app to observe the visual summaries related to eating rhythm on average 2.6±1.2 (Period 1); 2.2±1.4 (Period 2), and 1.8±1.3 (Period 3) times per day . Within F1=.039, P=.85; F1=.177, P=.68; and F1=.900, P=.35, respectively). The reported tendencies of emotional eating, uncontrolled eating, and cognitive restraint were higher after the intervention than before the intervention in both the intervention groups .There were no differences between groups regarding cognitive restraint, uncontrolled eating, or emotional eating before the intervention . More frequent discontent with eating habits during lunch time was reported after the intervention than before the intervention among all participants and among the members of the Healthiness group. There were no differences between the groups regarding the frequency of discontent after the intervention  during breakfast time, lunch time, afternoon, dinner time, late evening or in the night, or some other time, respectively.The participants were asked to evaluate how often they were discontent with their eating habits (1=never to 5=very often), and the average value for discontent was calculated for each meal time. In general, the participants were content with their eating habits 1.6-2.8, . The 2 gThe participants reported that they had obtained insights into their eating patterns during the intervention period. These were related to eating rhythm, variation in eating rhythm, and healthiness of eating habits .F1=.571, P=.45), and they did not change during the intervention .The measured attitudes toward health  did not differ between the groups before the intervention . Motives related to food choices remained unchanged within all the participants and subgroups except the increase in weight control motive in the Contentment group. No differences between the groups were identified after the intervention .The groups were similar regarding motives related to food choices before the intervention . However, the proportion of red button presses  was larger than that of yellow button presses (discontent).The 2 Button versions were developed and studied among 2 similar study groups. The only difference between the versions was the logic of the 2 buttons , indicated by different button colors. The content-discontent version was intended to provide the users freedom and feeling of autonomy to choose whether they are content with the eating occasion regardless of its healthiness and therefore to motivate the users toward behavior change -23. HoweIn both the study groups, the intensities of the 3 measured eating behavior tendencies  were elevated in postsurvey compared with presurvey. Cognitive restraint has been found to increase because of weight-loss interventions ,33, wherHigh tendencies for cognitive restraint, emotional eating, and uncontrolled eating are considered to associate with generally negative phenomena such as high BMI and stress -38. TherThe participants of both the intervention groups reported being more discontent with their eating during lunch time, after the intervention. This finding indicates that Button use made them more aware of lunch-time eating and the downsides of it . In the open-ended questions, some participants shared this view. In a best-case scenario, the increased discontent might trigger changes in the lunch-time eating habits: more time could be preserved for lunch, or better lunch options could be sought. However, unless some changes can be made, the increased discontent can be interpreted as a negative effect of using the app.A small increase in the weight-control motive was seen among the participants of the Contentment group, but there were no other changes in the food choice motives or attitudes. This is not surprising as attitudes are especially stable psychological constructs and difficult to be altered ,40. HoweSelf-monitoring of daily behavior is 1 way to become more aware of behavior, which in turn facilitates changes in behavior . A majorgrazing meal pattern.The study has limitations. First, there was no control group without the Button app, which limits the interpretation of the results. Part of the observed changes might have occurred because of study participation rather than the use of the app itself. Moreover, we cannot rule out the possibility that some external factors, such as season of the year, could have contributed to the observed changes in the study. Control would have also been useful for evaluating how the repetition of TFEQ after a relatively short period (30 days) influences the results . Second, the charm of the novelty of the app might have diminished toward the end of the intervention; therefore, the ease of remembering to mark every eating occasion might have weakened, influencing the number of daily presses. However, importantly, the reduction of daily consumed meals was observed in both survey data and data derived from the Button. Third, the intervention period of 30 days was relatively short. Engagement to the app use would have likely decreased over a longer period. However, we consider Button as a tool to become aware of temporal eating patterns rather than a tool for sustained use. Therefore, the period of 30 days is justified. However, a follow-up study would be especially useful to evaluate the long-term effects on eating behavior tendencies. Finally, Button data of 15 participants had to be excluded from the analyses. Complete case analysis was perceived as a more suitable approach than intention to treat with regard to Button data. The reasoning lies in the nature of the Button app, which is intended for voluntary use for those who are motivated to monitor their eating behavior. Therefore, including those who discontinued the app use might have led to very pessimistic results. On the other hand, complete case approach along with limited convenience sample might bias the results toward unjustified optimism. This bias is alleviated by the results derived from the survey data, which were based on the full sample. These results are in line with the observations from Button data. Considering the identified limitations, the future studies should include a control group, and there should be follow-up points to observe if the observed changes will sustain. The reported number of daily meals of the participants (4.5) was already as recommended in the Finnish nutrition recommendations—4 to 5 meals per day —before tThe Button app was easy to use, and adherence was good. The results indicate that self-monitoring of eating with a simple mobile app may hold promise in promoting regular eating patterns. However, the suitability of the app for users with different meal patterns and eating behavior tendencies needs further studies.


Soiling by dry deposition affects the power output of photovoltaic (PV) modules, especially under dry and arid conditions that favor natural atmospheric aerosols (wind-blown dust). In this paper, we report on measurements of the soiling effect on the energy yield of grid-connected crystalline silicon PV modules deployed in five cities across a north-south transect of approximately 1300 km in the Atacama Desert ranging from latitude 18°S to latitude 30°S. Energy losses were assessed by comparing side-by-side outputs of four co-planar PV modules. Two of the PV modules of the array were kept clean as a control, while we allowed the other two to naturally accumulate soiling for 12 months (from January 2017 to January 2018). We found that the combination of high deposition rates and infrequent rainfalls led to annual energy losses that peaked at 39% in the northern coastal part of the desert. In contrast, annual energy losses of 3% or less were measured at relatively high-altitude sites and also at locations in the southern part of the desert. For comparison, soiling-induced annual energy losses of about 7% were measured in Santiago, Chile (33°S), a major city with higher rainfall frequency but where urban pollution plays a significant role. Indeed, prior efforts based on available global datasets, a semi-empirical model has pointed the Atacama Desert as the place with the world’s highest solar irradiance3.Worldwide, the highest surface irradiance is expected to occur in summer at high altitude sites in the southern hemisphere near the Tropic of Capricorn4. Deployment of grid-connected PV systems aimed at self-consumption has been significantly slower, which may be related to some information barriers5. Indeed, the cost of operation and maintenance of PV systems aimed at self-consumption in the Atacama Desert is uncertain. In particular, few information on cleaning frequency requirements for counteracting soiling is available for end-users.The solar potential of the Atacama Desert see Fig.  has begu9. Dry deposition flux depends on dry deposition velocity9, which is determined by atmospheric properties as well as the characteristics of the dust and those of the deposition surface12. There are several models describing the dry deposition velocity of particles as a function of particle size (see a review10). Nevertheless, deposition velocity depends on wind speed and the humidity13. Higher wind speed increases friction velocity, which accelerates the transport of particulate matter (PM)10, while increases in relative humidity lead to hygroscopic growth, which can significantly increase the particle deposition rate10.Soiling generally arises from the dry deposition of dust and light absorbing particles on the surface of solar energy systems19 as well as with ground-based measurements of the AOD20.Dry deposition flux, and in turn the soiling rate, also depends on the PM mass concentration, which has been reported to be well correlated with satellite-derived estimates of the Aerosol Optical Depth (AOD)22 allow estimating for the Atacama Desert an annual climatological AOD slightly lower than 0.1 in the visible part of the spectrum. This relatively low AOD roughly agrees with ground-based measurements carried out at the Paranal Observatory 23. For comparison, the AOD at 500 nm is typically higher than 0.15 at sites in North Africa24, while the AOD in the visible range measured at desert sites in northern China ranges from 0.24 to 0.3625.Although few measurements are available for the Atacama desert, aerosols are expected to be abundant in the Atacama Desert due the dry and arid conditions that tend to favor wind-blown dust. However, satellite-estimates of the AOD in the visible range see Fig.  consisteIn this paper, we have measured the effects of soiling on the daily energy yield of grid-connected PV modules aimed at self-consumption, deployed in five relatively small cities across a north-south transect of approximately 1300 km in the Atacama Desert: Arica, Iquique, Calama, Copiapo, and La Serena. As shown in Fig. et al.26, we measured soiling losses by comparing side-by-side outputs of four co-planar grid-connected crystalline silicon PV modules fitted with micro-inverters. Two of the PV modules of the array were kept clean as a control, while we allowed the other two to naturally accumulate soiling for 12 months.Based on the method described by Gostein 27.We found that the combination of high deposition rates and infrequent rainfalls led to annual energy losses that peaked in the northern coastal part of the desert (39% in Arica and 18% in Iquique). In contrast, annual energy losses of 3% or less were measured at relatively high-altitude sites  and also at locations in the southern part of the desert (Copiapo and La Serena). These figures are consistent with the regional distribution of satellite-derived estimates of the AOD see Fig.  and with35. However, in the case of northern Africa and the Middle East38, reported energy losses due to soiling are comparable to those measured in the northern coastal part of the Atacama Desert.For comparison, annual energy losses due to soiling measured elsewhere are typically in the range of 1–7%Frequent cleaning may be necessary in Arica and Iquique, while in Calama, Copiapo and La Serena, the measured annual energy losses (3% or less) may make the need of cleaning the PV modules more than twice a year unlikely . Methodological details are provided below.26. However, in most of these cases, prior comparisons involved only two modules (we used four).Soiling losses were assessed by comparing side-by-side outputs of four co-planar grid-connected crystalline silicon PV modules (Hareon Solar Technology Co. Ltd. HR-265-18/Cbb 3BB). Each module was fitted with a micro-inverter (Sunny Boy 240-10) that allowed power output to be registered every 15 min. At each location, the tilt angle of the modules was selected according to the latitude. Two of the PV modules of the array were kept clean  as a control, while we allowed the other two to naturally accumulate soiling for 12 months. A similar approach to measure the soiling implies comparing the short-circuit currents (that can be considered a proxy for the effective irradiance received by the modules), or comparing the maximum power production of the modulesFigure The red curves in Fig. In the cases shown in Fig. energy fed into the grid by the modules.It can be also observed in both plots in Fig. daily changes in the SR of about 0.25% . Using the daily energy yield for computing the SR also allowed us to avoid dealing with the changes in the angular response of the PV modules depending on the cloud conditions  and comparing the daily energy yield (instead of the instant power output) helped to improve the signal to noise ratio (SNR) of our system such that we were able to detect daily energy yield agreed within a ±0.03 kWh margin (which was tested in the field prior to the experiment). Manual cleaning was carried out at each location at a regular basis . In the case of Calama, Copiapo and La Serena, the systems were deployed at airports/airfields such that the cleaning was carried out by the personnel assigned to the airports/airfields. In the case of Arica, Iquique and Santiago, the systems were deployed on university campuses such that university personnel, who were instructed for the task, carried out the cleaning. A team regularly inspected the systems for ensuring that the cleaning was reasonably uniform. Although, we did detect some flaws in the cleaning mostly due to the high personal rotation , we were unable to detect any significant affect of these minor flaws in our results.Note that although the modules/inverters were new and expected to be identical, we deployed at each location systems whose 26, authors compared temperature-corrected outputs of modules, we chose to apply no correction such that our results for SR accounted for both direct effects (radiation attenuation) and indirect effects (changes in the module’s temperature) of the soiling.The soiling led to differences not only in the power output but also in the temperature between the clean and the soiled modules. Although in prior effortsDaily Soiling Loss (SL) was taken as 1 - SR, while the monthly and annual soiling losses were computed by subtracting the monthly and annual energy fed into the grid by clean and soiled modules. Moreover, the daily deposition rate  was taken as being equal to the ratio between consecutive daily SR values.39. In the particular case of Santiago and Arica (the only Chilean locations where available), ground-based measurements of the AOD were obtained from the Aerosol Robotic Network (AERONET)40.Estimates of the AOD were retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) (aboard Terra satellite). We used the MIL3DAE daily level 3 product from MISR41. The global insolation data were obtained from the Surface meteorology and Solar Energy (SSE) web portal supported by the NASA Langley Research Center (LaRC) POWER Project42. Population and end-user electricity cost at each location was obtained from INE43.Precipitation data were obtained over the period 2004–2012 from the dataset of the Tropical Rainfall Measuring Mission (TRMM) of the Observations for Climate Model Intercomparison (obs4MIPs) projectFigure Figure Although the daily soiling rate changed during the year , the changes in seasonal averages were more significant in Santiago (where the average of the daily soiling rate in autumn-winter doubled with respect to the average in spring-summer) and in Calama (where the average of the daily soiling rate was approximately three times greater in spring-summer than in autumn-winter).Table Due to the impact of natural cleaning by rain on annual energy losses, the number of self-cleaning events at each location is also shown in Table 44. The influence of wind speed on the dry deposition velocity may in turn explain the positive correlation between the dry deposition flux and the measured wind speed in Riyadh City, Saudi Arabia45.A strong positive correlation between dry deposition velocity and wind speed has been found in previous studies, for example, in Guangzhou, ChinaFigure It can be observed in Fig. 15. This means that, although lowered wind speeds likely aid to lower the dry deposition velocity in autumn and winter, significant increments in the PM mass concentration negate such changes, thereby contributing to increases in the soiling deposition rate.At locations with an annual average of the soiling rate higher than 0.6%/day , which can significantly increase the particle deposition rateFigure It can be observed in Fig. At other locations where seasonal changes in the relative humidity tend to be less significant , the correlations in Fig. 9. This process occurs in many regions of the world having both high dust and humidity levels (that may lead to heavy morning dews). At high humidity, water-soluble dust particles on the surface form microscopic droplets of salt solutions that also retain any insoluble particles. When dried by subsequent evaporation, the precipitated salt acts as a cement to anchor insoluble particles to the surface forming a solid, packed cement-like composite9. We argue that the low levels of both humidity and aerosols (see next section) do not favor the cementation process in the Atacama Desert.It is worth noting that at the locations considered in this study we did not detect the cementation process described elsewhere10. Therefore, the daily deposition rate (i.e. the daily soiling rate) and the PM mass concentration are expected to be correlated.Although dry deposition flux is affected by wind speed and humidity (see prior sections), it also depends on particle concentration19 and also with ground-based measurements of the AOD46.In the case of Santiago, the Ministry of the Environment in Chile maintains an Ambient Air Monitoring Network, but these concentration measurements are not available at other locations considered in this study. However, PM mass concentration has been reported to be well correlated with satellite-derived estimates of the AOD15. Therefore, we tested the correlation between our daily soiling rate measurements and AOD estimates retrieved from satellites and ground-based measurements. Ground-based measurements of the AOD by AERONET-affiliated sunphotometers are available for Arica and Santiago. AOD measurements at 500 nm range from 0.15 to 0.2 in the case of Arica and from 0.1 to 0.2 in the case of Santiago.The correlation between PM mass concentration measurements and AOD retrievals has also been previously confirmed in SantiagoFigure Figure We used the linear regression shown in Fig. 47. In the case of the Atacama Desert, we found significant differences comparing the annual average of AOD values at 555 nm computed from retrievals of the MISR instrument over the period 2006–2016, with the annual average of AOD values at 550 nm derived from retrievals of the MODIS instrument onboard the satellite Terra over the period 2006–2017, especially in the coastal northern part of the Atacama Desert and the eastern slope of the Andes. These differences underlie the need for additional ground-based AOD measurements aimed at validating satellite estimates in the Atacama Desert.Indeed, estimates of the AOD retrieved from different satellite instruments show significant differences48). The same was true when comparing daily and monthly values  or when analyzing data of each location separately. There are several factors (including the dry deposition velocity) that can explain the weaker correlation between soiling rate and AOD estimate at monthly and daily time scales. However, as indicated above, the main drawback is likely due to the significant uncertainties associated with satellite–derived AOD estimates over the Atacama Desert.As show in Fig. 50, there are several points on this regard that we would like to highlight.Attribution efforts aimed at assessing the role of the tilt angle on the soiling rate imply comparing co-located (side-by-side) systems with PV modules at different tilt angles. Although those experiments were beyond the activities considered in our study, based on relevant prior efforts50 have shown that the transmittance decrease induced on PV modules by similar dust deposition is approximately 10% lower in the case of a tilt angle of 33° (like in Santiago) than in the case of a tilt angle of 18° (like in Arica).Since in our case the tilt angle of the modules was selected according to the latitude at each location, we expect that the difference in the soiling rate observed between Arica and Santiago should be partially due to the lower tilt angle of the PV modules in Arica (more than 10° lower than in Santiago). Indeed, prior effortsAlthough the tilt angle effect is indeed significant, it cannot explain the differences in the soiling rate between Arica and Santiago . A minor drizzle in early July changed the SR but it was insufficient for resetting the system. The lack of precipitation partially explains why the daily SL in Arica peaked at more than 80% by early December. By mid December the soiling losses in Arica were so significant that they led to a low SNR such that we manually cleaned the modules resetting the system on 15 December. This intervention was not needed at other locations since, as shown in Fig. 31, about half of the annual energy loss measured in Santiago , assessing site-specific precipitation thresholds for SR values lower than 0.9 may require years of observation. However, we expect the precipitation threshold for lower values of SR to be in the order of millimeters for the locations considered in this study. For comparison, 4–5 mm of rainfalls are needed to clean solar-tracking PV modules in Navarra, SpainThe effect of rainfall frequency on the soiling-induced losses can be assessed by comparing the actual annual energy losses . Indeed, cleaning the modules in Iquique, for example, every month, will not pay off because the increment in energy fed into the grid by frequent washing will not compensate the cleaning costs.Another approachFigure Table We have measured the effects of soiling on the daily energy yield of PV modules deployed in five cities across a north-south transect of approximately 1300 km in the Atacama Desert . For comparison, soiling-induced energy losses were also measured in Santiago (33°S), a major city with higher rainfall frequency and where urban pollution plays a significant role.Soiling losses were assessed by comparing side-by-side outputs of four co-planar grid-connected crystalline silicon PV modules fitted with micro-inverters. Two of the PV modules of the array were kept clean as a control, while we allowed the other two to naturally accumulate soiling during 12 months.Relatively high soiling rates (greater than 0.6%/day) were measured in Arica and in Santiago, while Calama and Copiapo exhibited relatively low soiling rates . The soiling rates measured in La Serena and in Iquique were typically around 0.16%/day and 0.31%/day, respectively. Moreover, the soiling rate changed significantly through the year in Santiago (where the daily soiling rate in autumn-winter doubled with respect to the average in spring-summer) and in Calama (where the soiling rate was nearly three times greater in spring-summer than in autumn-winter).The annual energy loss due to soiling depends on both the soiling rate and the precipitation frequency. For example, even though the daily soiling rates measured in Santiago and in Arica were comparable, the annual energy loss was about six times greater in Arica than in Santiago (39% versus 7%). This difference largely arises from the 23 rainfalls registered in Santiago in 2017 (versus only three drizzle events detected in Arica in 2017).Reducing soiling losses may require washing of PV modules but at locations such as Calama, Copiapo and La Serena, the measured annual energy losses (3% or less) may make cleaning the PV modules more than twice a year economically inconvenient. The combination of high soiling rates and infrequent precipitation events will likely make manual cleaning in Arica and Iquique necessary. Although the optimal washing frequency will depend on the monetary cost of cleaning, we estimate that in the absence of precipitation and considering a cleaning price in the range US$5–10/kWp, solar modules should be cleaned every 3–4 months in Iquique and every 45–75 days in Arica.


We performed this study to investigate the possibility of a definitive pattern of Galectin-3  expression in the cerebrospinal fluid (CSF) and serum of Alzheimer’s disease (AD) and Amyotrophic Lateral Sclerosis (ALS) patients. In our study, we collected the CSF and serum samples of 31 AD patients, 19 ALS patients and 50 normal healthy subjects (controls). Quantitative ELISA measured Gal-3 concentrations in CSF and serum samples. A comparative analysis was performed to analyze and understand the Gal-3 expression pattern. A number of neuropsychological assessments and statistical analyses were carried out to validate our findings. Recent researches have established the role of galectins in various neurodegenerative disorders (NDDs), but a definitive pattern of galectin expression is still elusive. A significant difference was observed in serum and CSF Gal-3 concentrations between AD patients and healthy controls. The difference in serum and CSF Gal-3 concentrations between ALS patients vs. controls was lesser as compared to AD patients vs. controls. The difference in serum and CSF Gal-3 concentrations of AD vs. ALS patients was not significant. The MMSE score and serum and CSF Gal-3 concentrations in AD and ALS patients, and controls exhibited a significant positive correlation. The findings of the present study are expected to provide an insight into the definitive pattern of Gal-3 expression in AD and ALS patients, and might thus establish Gal-3 as a strong biomarker. This in turn will open up new and promising research avenues targeting the expression of galectins to modulate the progression of NDDs, and pave the way for novel therapeutic options. Galectins are mammalian class of an otherwise large family, lectin, and characterized as glycoproteins, which have been reported to be expressed in almost all vital organs and involved in almost all significant biological functions . Gal-3 hThe idea of the proposed research study cropped from the above-mentioned reports suggesting altered expression level of Gal-3 in various NDDs. The detrimental role of Gal-3 in diseases like ALS and AD suggest that lysosomal dysfunctions combined with reduced autophagy can further stimulate neurodegeneration. Elevated levels of Gal-3 in serum and CSF might induce enhanced inflammation, apoptosis and neurodegeneration. There are few individual studies on either serum or CSF, which suggest Gal-3 as possible biomarker of NDDs. The findings of the present study suggesting a definitive Gal-3 expression pattern in serum as well as CSF samples of AD and ALS patients will firmly affirm the role of Gal-3 as potential biomarker for these diseases.This study was carried out in accordance with the recommendations of National Institute of Neurological and Communicative Disorders and Stroke AD and Related Disorders Association and International Classification of Diseases, Tenth Revision, Diagnostic and Statistical Manual of Mental Disorders (Third Edition) . The KAUThe AD (31) and ALS (19) patients as well as healthy individuals (50) serving as controls were recruited from Division of Neurosurgery, College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia under the careful supervision of Prof. Saleh Baeesa. Each participant was requested to provide written informed consent. The clinical severity of cognitive status was assessed by the standard MMSE  . All theCSF as well as serum samples were collected from 31 patients with AD and 19 patients with ALS. The collected samples were frozen at −80°C until assayed. Samples were also collected from 50 healthy elderly individuals, which served as controls. For CSF sampling, lumbar puncture (LB) using 20G spinal needle was performed using a 20 Gauge, 3.5 inch Quincke point spinal needle  under local anesthesia in recumbent position at the L3/L4 spine interspace or below of each subject. Every CSF sample collected was approximately 5–8 ml in volume. The CSF samples were collected in Falcon polypropylene tubes . The serum as well as CSF samples were collected early in the morning between 8 and 9 am. The collected CSF samples were centrifuged at 2500 rpm for 5 min to exclude blood contaminants. The samples were then aliquoted in smaller concentrations of 250 μl per aliquot and frozen instantly at −80°C until used for further analyses. While performing the experiments, we freeze thawed only the required number of aliquots as per the requirements of sample concentration needed for the experiment. All the patients and control individuals were fasting since the evening before at the time of LP, and were requested to abstain from smoking. Most of the CSF and serum samples were analyzed within 8 weeks of their collection. This duration was mainly attributed to the fact that before opening an ELISA kit, we made sure that we have collected at least 20 cumulative samples of CSF and serum.All the AD and ALS patients included in this study were diagnosed with the disease since at least 2 years.Only those patients and controls were included who gave their consent for the study either by themselves or with the help of their close kin.All the subjects under the prescription of NSAIDs or any other drugs interfering with the neuroinflammatory system at the time of sample collection were excluded.All the subjects reported for chain smoking were excluded; the information was confirmed by accompanying kin.An important limitation of our study is relatively small sample size as compared with those used by large-scale multicentric studies.We collected the serum and CSF samples in a span of 2 years from almost all the NDDs patients like AD, PD, ALS, MCI, VaD, and MS based on their consent.For this study, we only selected the two group of patients with maximum number of samples, and those turned out to be AD (31 patients) and ALS (19 patients). The PD (7 patients), MCI (9 patients), VaD (7 patients) and MS (4 patients) were much lesser in number, and we chose to not include them in this study and wait for more samples to design an effective analyses.in vitro SimpleStep ELISATM kit (ab188394). Briefly, 50 μL of all samples was added to respective wells of polystyrene 96-well plates which were pre-coated with antibodies against Gal-3 , followed by the addition of 50 μL antibody cocktail to each and every well. The treated plate was then sealed followed by incubation at RT (room temperature) for 1 h, and finally shaked at 400 rpm on a plate shaker at RT for 2 h. The plate was then washed three times with 350 μL wash buffer, the plate was inverted and blotted against clean paper towels to remove excess liquid. This was followed by the addition of 100 μL TMB substrate to every well, and incubation at 400 rpm on a plate shaker for 10 min in dark. In the last step, each well was added with 100 μL stop solution, and the plate was shaken for 1 min to mix the solutions. The endpoint reading was recorded at 450 nm in ELISA reader .The concentrations of Gal-3 in serum, CSF and control samples were measured by Abcam’s Galectin-3 post hoc test between groups. Chi square tests were used to analyze the differences in gender distribution. The differences in scores of baseline and follow up neuropsychological tests for AD and ALS groups were analyzed by LSD test. ROC curves were built to determine the sensitivity (SE), specificity (SP), and area under the curve of Gal-3 biomarker for the discrimination between AD and controls, and ALS and controls.The differences in cognitive performance, sociodemographic characteristics, and Gal-3 concentrations were analyzed by ANOVA test, with LSD tests for pairwise In relation to the present study, Gal-3 has been reported to be involved in the physiology of almost all types of immune cells . The infTable 1. The results of two consecutive neuropsychological evaluations performed at an interval of at least 1 year have been summarized in Table 2. In the ALS group, the scores in all neuropsychological tests remained stable over time, whereas in the AD group, there was a decline in TMT-A, FOME, and SKT scores in the same period.The AD patients were on average older than ALS patients were, and thus scored lower on MMSE test. No significant difference was found in years of education and gender distribution between AD, ALS and controls groups. The baseline characteristics and demographic parameters have been summarized in Table 3). Serum Gal-3 concentrations >8.32 ng/mL (SE = 87% and SP = 74%) and CSF Gal-3 concentrations >7.64 (SE = 83% and SP = 69%) best differentiated the AD, ALS and control groups. We also investigated whether the Gal-3 concentrations might alter the diagnostic accuracy. Figure 1 represents the distribution of subjects according to the serum and CSF Gal-3 concentrations in AD, ALS and control groups. Our findings suggested that the AD patients had a distribution almost similar those of ALS patients.Based on SE and SP values, ROC curves were drawn to determine the cutoff scores for serum as well as CSF Gal-3 concentrations (P = 0.005). Remarkable difference in the serum Gal-3 concentrations was observed between AD and ALS patients during the LSD test. Figure 2 depicts that the difference in the serum Gal-3 concentrations between AD patients and healthy controls was significant . Similarly, Figure 3 depicts that the difference in serum Gal-3 concentrations between ALS patients and controls was significant , but the difference was lesser as compared to AD vs. controls. Figure 4 depicts that the difference in serum Gal-3 concentrations of AD and ALS patients was not significant . Figure 5 depicts that the MMSE score and serum Gal-3 concentrations in AD and ALS patients, and controls exhibited a significant positive correlation .In order to incorporate the difference in age as a factor between AD and ALS patients, and controls; age was used as ANOVA covariate. Visible differences were observed in serum Gal-3 concentrations between the three different groups  (P = 0.005). Remarkable difference in the CSF Gal-3 concentrations was observed between AD and ALS patients during the LSD test. Figure 6 depicts that the difference in the CSF Gal-3 concentrations between AD patients and healthy controls was significant . Similarly, Figure 7 depicts that the difference in CSF Gal-3 concentrations between ALS patients and controls was significant , but the difference was lesser as compared to AD vs. controls. Figure 8 depicts that the difference in CSF Gal-3 concentrations of AD and ALS patients was not significant . Figure 9 depicts that the MMSE score and CSF Gal-3 concentrations in AD and ALS patients, and controls exhibited a significant positive correlation .In order to incorporate the difference in age as a factor between AD and ALS patients, and controls; age was again used as ANOVA covariate. Visible differences were observed in CSF Gal-3 concentrations between the three different groups   as well as CSF samples  of AD and ALS patients. This enhanced expression of Gal-3 can be attributed to the activation and regulation of immune system in AD and ALS patients  as well as CSF samples  between AD and ALS patients was not significant. This finding seems evident as the Gal-3 expression is altered to a considerable degree in AD as well as ALS patients, in reference to the control samples. This finding also suggests that Gal-3 is an aspecific marker that will discriminate every neurodegenerative/neuroinflammatory-related condition as opposed to a control population, and lacks specificity to discriminate between various dementias. A significant positive correlation between serum and CSF Gal-3 levels and MMSE score was observed . The scores of neuropsychological tests remained stable in ALS patients over time, whereas in the AD group, there was a decline in TMT-A, FOME, and SKT scores in the same period (Table 2). This finding is well received, as there is hardly any memory loss is ALS patients, whereas there is a rapid loss of memory and cognitive skills in AD patients.The findings of our study demonstrated that the expression pattern of Gal-3 was significantly enhanced as compared to the controls in serum . The secretory nature of Gal-3 and its detectable level in serum and CSF strongly proclaim this dynamic molecule as potential biomarker for AD and ALS in particular, and other NDDs in general. Our interesting findings suggest the need to explore and investigate the role of other galectins  in various NDDs. These crucial findings strongly entail the need of future evaluation in prospective patient’s cohort, which in turn can put galectins, especially Gal-3, in clinical trials.GA conceived the study, performed all the experiments, analyzed the data, and compiled the whole manuscript. SB provided the CSF and serum samples.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.


The global burden of diabetic retinopathy (DR) continues to worsen and DR remains a leading cause of vision loss worldwide. Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) acquired at a single visit from a patient with DR. The proposed DL models were designed to predict future DR progression, defined as 2-step worsening on the Early Treatment Diabetic Retinopathy Diabetic Retinopathy Severity Scale, and were trained against DR severity scores assessed after 6, 12, and 24 months from the baseline visit by masked, well-trained, human reading center graders. The performance of one of these models (prediction at month 12) resulted in an area under the curve equal to 0.79. Interestingly, our results highlight the importance of the predictive signal located in the peripheral retinal fields, not routinely collected for DR assessments, and the importance of microvascular abnormalities. Our findings show the feasibility of predicting future DR progression by leveraging CFPs of a patient acquired at a single visit. Upon further development on larger and more diverse datasets, such an algorithm could enable early diagnosis and referral to a retina specialist for more frequent monitoring and even consideration of early intervention. Moreover, it could also improve patient recruitment for clinical trials targeting DR. Almost all patients with type 1 diabetes mellitus and ~60% of patients with type 2 diabetes mellitus will develop retinopathy during the first 20 years from onset of diabetes.2 However, DR often remains undetected until it progresses to an advanced vision-threatening stage. The current state of DR screening in the real world, based on assessment of color fundus photographs  may offer a solution to this conundrum. Deep learning (DL), and specifically, deep convolutional neural networks (DCNNs),19 and to assess the feasibility of developing DCNNs operating on 7-field CFPs that can predict the future threat of significant DR worsening at a patient level over a span of 2 years after the baseline visit.The purpose of this work was to go beyond the use of DL for DR diagnostics20 from large controlled clinical trials. Previous studies have limited the deployment of DCNNs to fovea- or optic nerve–centered CFPs.19 Our findings highlight the importance of the predictive signal located in the peripheral retinal fields of patients with DR and suggest that such a predictive algorithm, upon further development and proper validation, could help fight blindness by identifying fast DR progressors for referral to a retina specialist or inclusion in clinical trials targeting early stages of DR.To achieve that, our DCNNs have been trained on high-quality 7-field CFPs acquired at a single visit and graded for DR severity by masked and well-trained reading center experts, using the validated Early Treatment Diabetic Retinopathy Study (ETDRS) Diabetic Retinopathy Severity Scale (DRSS)(P = 0.00049) and 24 (P = 0.00042). Results for month 6 were weaker compared with months 12 and 24, mainly due to the scarcity of patients with DR progression within the first 6 months .The DL models , 0.64 ± 0.05 , and 0.69 ± 0.05  for months 6, 12, and 24, respectively.The optic-nerve-centered field (F1) and the fovea-centered field (F2) are generally regarded as the most important fields in a standard ophthalmoscopy exam. The purpose of this analysis was to evaluate the predictive signal of DR progression in these central retinal fields compared with the peripheral fields . We found that the main predictive contribution came from the peripheral retinal fields  that encompass areas of the retina far from both the fovea and optic nerve. Performance comparisons between models trained only on central fields (F1 and F2) versus models trained on all seven retinal fields support our finding. For this comparison, we performed the random forest (RF) aggregation only with the probabilities generated by the F1- and F2-specific DCNNs Fig. . Using t21 allows for interpretation of predictions of complex symbolic machine learning (ML) models by attributing the descriptors to the weights of importance. Here it was used to evaluate the field-specific DCNN contribution to the final RF aggregation. Figure x-axis) and that low probabilities (dots in the blue spectrum) result in the opposite. This pattern is broken in the few cases where the aggregation used low- and high-probability values to predict both classes. This further confirms that using just one field would not be sufficient to properly address the prediction. In this particular case, Fig. The analysis by “Shapley Additive Explanations” (SHAP)25 are a powerful strategy to shed light on the complex mechanism inside a single DCNN. These maps highlight areas where the model focuses its attention in order to decide how to classify a certain query image. Attribution maps are useful to check whether the decision mechanism of a DCNN is relatable to human expectation. The maps analyzed in this study were generated by guided back propagation,24 which provided the most salient results among different attribution techniques that were tested.Attribution maps26 A remarkable aspect is that the DCNNs are able to highlight very small and low-contrast microaneurysms, even though they were not explicitly designed to accomplish this task. The correlation between microaneurysms and DR progression has been studied by Piri et al.27 and our attribution maps seem to be in line with these studies. However, there is an important difference; in our work, the DCNNs learn a functional mapping from baseline to month X  using a single time point rather than the multiple visits used by Piri et al.27 to measure microaneurysm turnover. Please see the Supplement for examples of how attribution maps on the same image vary from month 6 through 24  will have an ~20% risk, whereas a patient with severe NPDR will have an ~50% risk of progressing to proliferative DR (PDR) within 1 year.20 However, it is not know which individual patients belong to the subsets of fast versus slow progressors. This type of individual prediction can be achieved by means of the DL algorithms described in this paper.This work demonstrates the feasibility of developing a DL algorithm to identify patients who will experience DR worsening by two or more ETDRS DRSS steps over the next 2 years, based solely on CFPs acquired at a single visit. Currently, it is only possible to approximate progression risk for groups of patients with similar signs and symptoms on the basis of their assessed DR severity level, but it is not possible to accurately predict the course of DR in an individual patient.19 Moreover, lateral fields may contain predictive signs of DR worsening before the pathology has affected the macula, allowing for prompt referral and, eventually, timely treatment before vision loss occurs. Coupling algorithms such as the one described here with more recent imaging technologies, such as ultrawide field (UWF) photography, might enable the identification of fast DR progressors even earlier in their disease course. The non-mydriatic UWF CFPs30 were recently validated for DR severity assessments and have the advantage of capturing a view of 200 degrees of the retina with less time and effort compared with the standard 7-field CFP, while providing good to excellent agreement in determining DR severity.30 Part of our algorithm validation and expansion strategy is to guarantee that the described algorithm will be capable of operating on UWF CFPs as well.A second crucial finding of this study was that any imaging-based diagnostic/predictive AI tool for DR should contemplate the inspection of both central and peripheral retina, instead of being limited to the use of CFPs centered around the fovea or optic nerve .31 The advantage of using clinical trial data is the availability of high-quality standardized imaging formats and processes as well as assessments by masked experts at a centralized reading center. However, this means that, at this time, our work is only applicable to clinical trial populations within a similar range of pre-specified eligibility criteria. Therefore, validation with datasets acquired in the real world will be essential to ensure that these results are reproducible and applicable to the broader DR population; the authors are already tackling this issue. The great advantage of the dataset at our disposal is that it contains a representation of patients across all levels of DR severity .19Our work represents a step forward in the application of AI to the field of ophthalmology. The DL modeling presented here differs from previously mentioned studies which either required multiple visits, did not employ ML, or focused on current DR severity diagnosis instead of prediction of the future. Additionally, as highlighted before, peripheral CFP fields have not been used in most of the previous ML and DL approaches dedicated to DR diagnostic development.32 However, these estimates are based on the ability to expand diagnosis across members of the diabetes population currently lacking access to medical care. To our knowledge, nobody has yet performed an evaluation of cost savings based on predictive screening, which would identify individuals who require immediate follow up and possible early intervention due to impeding near-future progression. Deployment of a predictive algorithm represents therefore an important conceptual leap for the efficient triage of patients at high risk of vision-threatening DR complications and a step towards a personalized approach to medicine, what it also envisaged as “precision medicine.”Our findings suggest that deployment of a predictive DR progression algorithm would enable early identification of patients at highest risk of vision loss, allowing timely referral to retina specialists and potential initiation of treatment before irreversible vision loss occurs. In the context of the global diabetes epidemic, new screening tools could have a substantial socioeconomic impact on the healthcare system. The federal savings are projected to reach $624.0 million annually in the United States alone, if we can deploy tools that enable recommended eye screening and ophthalmic care for all diabetes patients.Moreover, the identification of fast DR-progressing patients through a predictive DR progression algorithm would have the potential to support the development of new treatments targeting patients with mild and moderate NPDR. Clinical trials based on the traditional endpoint of DR improvement/worsening are considered prohibitively expensive due to size and/or duration. By enriching the clinical trial population with fast DR-progressing individuals, such an AI-based recruitment strategy would increase the chances of success for clinical trials of novel drugs designed to address the unmet need of those members of the early DR population at the greatest risk of progression and vision loss. This is particularly important considering the rising global prevalence of DR and its potential impact on healthcare systems and society.35 and RISE (NCT00473330)35 phase 3 studies at baseline (start of the studies) that were not treated with anti-vascular endothelial growth factor (VEGF) therapy. RIDE and RISE were registered on 13/05/2007 with the title “A Study of Ranibizumab Injection in Subjects With Clinically Significant Macular Edema (ME) With Center Involvement Secondary to Diabetes Mellitus” and can be accessed via the ICTRP portal at the following URLs: RIDE at http://apps.who.int/trialsearch/Trial2.aspx?TrialID=NCT00473382, RISE at http://apps.who.int/trialsearch/Trial2.aspx?TrialID=NCT00473330.This study is based on the post hoc retrospective analysis of stereoscopic 7-field CFPs obtained from eyes with DR in the RIDE (NCT00473382)34 Baseline ETDRS DRSS DR severity in RIDE/RISE sham-treated study and fellow eyes ranged from 10 (absent) to 71 (high-risk PDR) .20 The manually detected rates of two-step or more worsening in sham study eyes and fellow eyes at 2 years from baseline were 9.6% and 11.7%, respectively.33 The majority of the CFP images were of high quality due to the training requirement for all study site photographers who participated in CFP acquisition. Additionally, the image assessment for the manual severity grading was of the highest attainable quality because it was performed by two masked readers at an accredited fundus photograph reading center supervised by a senior reader in charge of adjudication when needed.The objective of this analyses was to generate algorithms that can predict worsening in untreated eyes from baseline over a period of 2 years. From RIDE and RISE, only the baseline images from those eyes that were randomized to sham/untreated groups and the untreated fellow eyes were used for this work. These eyes had the natural course of DR worsening outcomes without anti-VEGF treatment collected at months 6, 12, and 24. There was a total of 529 , 528 , and 499  patients with untreated eyes who had all seven fields captured on CFP at months 6, 12, and 24. RIDE and RISE were two parallel, identically designed, phase 3, randomized, double-masked clinical trials of ranibizumab in patients with DR with diabetic macular edema. The studies were sham injection controlled for 2 years and followed for an additional year in which all sham patients crossed over to ranibizumab therapy. The study design, eligibility criteria, and core efficacy and safety outcomes of these trials have been previously described.35Each patient data point at each visit consists of seven CFP fields that span a 30-degree view of retina. The different fields are indicated with the following codes: F1 (optic nerve centered); F2 (fovea centered); and F3, F4, F5, F6, and F7 ; all codes correspond to a general ETDRS standard adopted by reading centers.The trials adhered to the tenets of the Declaration of Helsinki and were Health Insurance Portability and Accountability Act compliant, and the protocols were approved by the study sites’ institutional review boards and ethics committees (or as applicable). Patients provided written informed consent.The 7-field CFPs acquired for each patient at baseline were used to train DL models designed to predict, on an individual patient level, two-step or more worsening with respect to the ETDRS DRSS over 2 years, specifically after 6, 12, and 24 months. The problem under study is a binary classification, where ‘0’ means no occurrence of two-step or more ETDRS DRSS worsening at month X , and ‘1’ means the opposite. The incidence of sham study and fellow eyes diagnosed with worsening by two or more ETDRS DRSS steps was ~6% at month 6, ~10% at month 12, and ~12% at month 24. The histograms in Supplementary Fig. 36 Moreover, the incidence of clinically significant diabetic macular edema was shown to correlate with the progression of DR on the ETDRS DRSS from NPDR to PDR.20 Even just one-step or more DRSS worsening was shown to be associated with a five- to sixfold increased risk of PDR development, and a three- to fourfold risk of developing clinically significant macular edema with a high likelihood of vision loss over the period of 4 years.37 However, considering the intergrader variability associated with this scale, two steps or more on the ETDRS DRSS was deemed a more robust outcome variable to use for predictive modeling.The ETDRS DRSS scale has been validated and widely used for objective quantification of retinopathy severity in the clinical trial setting. The landmark trials, Diabetic Retinopathy Study and ETDRS, established that worsening of DR (that is measured by anatomic features on the ETDRS DRSS) is significantly associated with development of severe vision loss.The overall model is a binary classification whose input data points are sets of seven CFP images acquired for a selected eye at baseline. Modeling was performed in two steps: (1) DCNNs were separately trained for each type of CFP field to form the “pillars”; and (2) the probabilities provided by the individual pillars are then aggregated by means of RFs. Single pillars and RFs are trained against the binary outcome variable defined in the previous section. A simple schematic of the model is provided by Fig. 38 was used to build the field-specific pillars. A transfer learning39 cascade strategy was adopted to create the starting weights; first, the architecture was initialized with Imagenet40 weights and trained on the Kaggle DR41 dataset to differentiate between CFPs with no DR from those with signs of DR; the weights generated by this last training are then used to initialize the training of the pillars for DR progression.The Inception-v3 architectureTransfer learning was performed, first by replacing and training for 10 epochs the last dense layers while keeping all the others fixed, and then by fine-tuning for 50 epochs all layers from the end up to the third one. The Adam optimizer was used with learning rates adapted for the various pillars. A small parameter grid search was conducted to find the optimal learning rates for the pillars. A five-times fivefold CV scheme with patient ID constraint  was adopted to generate a total of 25 DCNNs for each pillar.42 were then used to aggregate the probabilities of all pillars. A five-fold CV scheme with the same folds of the previous step was performed. This means that for each fold, 35 probabilities (seven fields × five repetitions for each CV fold) were used as input features for the RF. Please see Supplementary Methods for additional information about the RF models used in this study.RFs43 which is obtained by maximizing the difference between the true- and false-positive rate.The model performance was measured in terms of AUC, sensitivity, and specificity evaluated at the Youden’s point,21 The traditional feature importance algorithm is based on the Gini index highlights, which features are contributing the most to the prediction across the entire population44 and is known in literature to be characterized by multiple biases,45 preventing this algorithm to be reliable in many scenarios. Differently from the Gini index, SHAP offers a local perspective by informing on the most predictive features at the level of individual sample.21 In the plots of Fig. SHAP is a technique originally adopted in game theory to determine how much each player in a collaborative game has contributed to its success. In the ML context, each SHAP value measures how much each feature contributes to the target prediction, either in a positive or a negative way.46 The idea behind gradient-based methods is that the relative importance of the input features is measured by computing the gradient of the output decision with respect to those input features.47 This gradient, once back-projected onto the input image, provides an idea of where the CNN has focused on in order to classify the image in a certain way. In particular, guided-backpropagation46 is characterized by the suppression of flow of gradients where either the input or incoming gradients are negative.The attribution maps for the individual DL models presented in Fig. Further information on research design is available in the Supplementary InformationReporting summary


ELANE mutations is a rare disease characterized by very low numbers of circulating neutrophils. Little is known about the functional characteristics of the SCN1 granulocytes, except that eosinophilia has been noticed in both bone marrow and peripheral blood. In this study, we profiled the number and function of granulocytes in patients suffering from SCN1.Neutrophils and eosinophils are multifunctional granulocytes derived from common myelocytic-committed progenitor cells. Severe congenital neutropenia 1 (SCN1) caused by Nine patients diagnosed with SCN1 were enrolled in this study and absolute counts of eosinophils and neutrophils from bone marrow aspirates and peripheral blood samples were analysed. In addition, Ficoll-Paque enriched granulocytes from patients and healthy controls were analysed for specific eosinophil and neutrophil markers using flow cytometry and for NADPH-oxidase activity-profile by chemiluminescence.Our data demonstrate a skewed granulocyte population in SCN1 patients dominated by eosinophils in both bone marrow and peripheral blood. The latter was detected only by blood smear examination, but not by automated blood analysers. Furthermore, we show that the SCN1 eosinophils exerted normal production of reactive oxygen species generated by the NADPH-oxidase, however the response was profoundly different from that of healthy control neutrophils.ELANE mutations suffer from neutropenia yet display eosinophilia in the bone marrow and blood, as revealed by smear examination but not by automatic blood analysers. The SCN1 eosinophils are functionally normal regarding production of reactive oxygen species (ROS). However, the ROS profile produced by eosinophils differs drastically from that of neutrophils isolated from the same blood donor, implying that the eosinophilia in SCN1 cannot compensate for the loss of neutrophils regarding ROS-mediated functions.SCN1 patients with  ELANE gene that encodes neutrophil elastase, a serine protease synthesized at the promyelocyte stage during granulopoesis and stored in the azurophil granules . He. He9/L) 6 cells (P02) up to 3.76 × 106 cells (P05 and P06 after G-CSF treatment). All experiments were performed on granulocytes from patients without G-CSF treatment if not indicated. To elucidate the presence of neutrophils and eosinophils in these granulocyte samples, we first screened for neutrophils by measuring MPO in P04, P05 and P06. MPO is a peroxidase abundantly present in neutrophil azurophil (primary) granules and monocytes, but not in eosinophils. The MPO levels in the SCN1 granulocytes were significantly decreased as compared to granulocyte samples from healthy controls , and that the majority of these (79%) were eosinophils  and not neutrophils  were higher in the PBMCs of patients compared with those from controls , and CD49d (expressed on eosinophils but not on mature neutrophils) to classify cells isolated from two SCN1 patients. In agreement with the above results, a large proportion (~ 90%) of the SCN1 granulocytes were eosinophils  infection which may be explained by an increased prevalence of TB in China [In this study, we examined the composition and functionality of granulocytes obtained from nine SCN1 patients representing eight different e marrow , 6, 14. in China  and thatin China .All our data point out that SCN1 granulocytes are dominated with eosinophils except the data obtained from automatic blood analysers showing much higher counts of neutrophils than eosinophils. Our data are in line with previous reports showing maturation arrest at the promyelocyte stage in association with hypereosinophilia in SCN1 , 14, 15.By using the Ficoll-Paque separation method, we recovered granulocytes from the peripheral blood of SCN1 patients. This separation method is very commonly used to enrich neutrophils from healthy blood donors as neutrophils are the dominating granulocytes in healthy control blood. However, as the granulocyte composition in SCN1 patients is skewed to eosinophils, their Ficoll-Paque enriched granulocyte population mainly contain eosinophils. We confirmed this by the feature of eosinophils being auto fluorescent in the FITC channel and by cell surface specific mAbs against neutrophils and eosinophils Figs. . FurtherOur functional characterization of the SCN1 granulocytes, measured as NADPH-oxidase activity, demonstrate that these cells display a ROS production profile almost identical to that of purified control eosinophils. Apart from corroborating that the SCN1 granulocyte population is dominated by eosinophils, these data contribute with the novel fact that SCN1 eosinophils have a normal functional NADPH-oxidase which has not been reported before. Moreover, the NADPH-oxidase activation profile in the SCN4 granulocytes (dominated with neutrophils) was very similar to that of healthy control granulocytes. This further supports the notion that increased eosinophils is not a general feature for all types of SCN. NADPH-oxidase derived ROS are essential not only for bacterial killing, but also play a critical role in cell signaling and can, if not properly controlled, cause tissue damage. The precise mechanism of NADPH-oxidase assembly and activation in neutrophils and eosinophils is poorly understood, but our data showing a difference in ROS production between these two types of granulocytes support the fact that eosinophils and neutrophils have different roles in host defence. This implies that the increased eosinophils in SCN1 patients cannot fully compensate for the loss of neutrophils regarding the oxygen-dependent killing machinery.G6PC3 gene, our data derived from this patient clearly show that SCN4 has a different granulocyte composition from that of SCN1 patients. This thus suggests that increased eosinophil counts is not a general compensation mechanism for all types of SCN. This could depend on the underlying mechanism of the neutropenia, SCN4 is for example not always accompanied by a maturation arrest of neutrophil precursors [It remains to be elucidated why SCN1 patients have increased eosinophils. Neutrophils and eosinophils are derived from a common myelocytic-committed progenitor, the myeloblast, but driven to maturation by different combinations of transcription factors, cytokines, and localized niches in the bone marrow , 29. Strecursors . It coulecursors , 34.Our in-depth characterization of SCN1 blood granulocytes clearly demonstrates a skewed granulocyte composition with increased eosinophils in circulation, which is evident by blood smear analyses but not by automated blood analysers. Accordingly, SCN1 granulocytes enriched by Ficoll-Paque isolation are dominated by eosinophils. The exact mechanism underlying the eosinophilia in SCN1 remains to be investigated. However, the data presented in this study shows functionally normal NADPH-oxidase activity by SCN1 eosinophils, that differs considerably from that of healthy control neutrophils. This indicates that the increased eosinophils in SCN1 patients cannot compensate for the loss of neutrophils regarding the NADPH-oxidase dependent microbial killing machinery.


Corrigendum:After publication, the authors noticed that the wrong version of Figure 3 was inadvertently published. We have replaced this figure with the correct version. In the correct figure, labels for HIV-1 Peptides, HIV-2 Peptides, and Biotinylated rAg have been removed from the blocker pad and legend.The authors also noticed a typographical error in the third paragraph of the Discussion section. "... able to detect seroconversion an average of 1.5 days earlier than automated assays" has been amended to "... able to detect seroconversion an average of 1.5 days later than automated assays."