Artibeus jamaicensis), have been observed to engage in cooperative mate guarding, and thus might be expected to display prosocial behavior as well. However, frugivory and hematophagy diets may impose different selection pressures on prosocial preferences, given that prosocial preferences may depend upon cognitive abilities selected by different ecological constraints. Thus, we assessed whether Jamaican fruit bats would assist a conspecific in an escape paradigm in which a donor could opt to release a recipient from an enclosure. The test apparatus contained two compartments—one of which was equipped with a sensor that, once triggered, released the trap door of the adjacent compartment. Sixty-six exhaustive pairs of 12 bats were tested, with each bat in each role, twice when the recipient was present and twice when absent. Bats decreased their behavior of releasing the trapdoor in both conditions over time, decreasing the behavior slightly more rapidly in the recipient absent condition. Bats did not release the door more often when recipients were present, regardless of the recipient; thus, there was no clear evidence of prosocial behavior.Once thought to be uniquely human, prosocial behavior has been observed in a number of species, including vampire bats that engage in costly food-sharing. Another social chiropteran, Jamaican fruit bats ( Desmodus rotundus), in which individuals who have fed will regurgitate a portion of their blood meal to feed group-mates who have not fed [One of the most enthusiastically studied questions in the field of comparative psychology over the last decade is the question of whether any non-human species share the human tendency to behave prosocially when there is no gain to the self. Prosocial behavior is defined as a behavior performed by one individual to improve the welfare of another individual . Prosoci not fed . In the Rhinolophus darling), a small insectivorous bat, exhibit a behavior known as group augmentation, in which offspring of the group huddle together in cave crèches so that they can stay warm through the thermoregulation of the group [Vampire bats are likely not the only bat species to engage in prosocial or cooperative behaviors. Darling’s horseshoe bats (he group . Carter he group  suggest Phyllostomus hastatus), a small omnivorous bat [Phyllostomus hastatus groups have synchronized births [Another instance of female bats cooperating together to ensure the survival of the group offspring is pup guarding, which can be observed in greater spear-nosed bats  housed at the Organization for Bat Conservation . Bats were segregated by sex and housed in flight cages that allowed them to freely associate with other members of their own and other species. Our sample contained two mother daughter pairings. Bat F092 was the mother of bat 346B and bat AF86 was the mother of bat 2734. No other bats in our sample were related along the matrilineal line. These subjects shared a flight cage with female short tailed fruit bats (Carollia perspicillata), another frugivorous bat. All animals were either micro-chipped with passive integrated transponder (PIT tags) that could be scanned by the researcher to determine their identity, or were banded on their wing, which could also be used to determine their identity. The study was approved by the Institutional Animal Care and Use Committee of Oakland University, Approval #15093.Subjects consisted of 14 adult female Jamaican fruit bats . Those bats that were able to exit the apparatus on two consecutive trials moved on to Phase Two. Training continued until 12 bats reached criterion. All possible dyads of the 12 bats that met criteria (N = 66 dyads) were systematically paired for four trials (two RA and two RP) within the apparatus. Dyads were tested in random order; however, no bat was tested in more than one dyad per day. Bats within a dyad participated as both donor and recipient on alternating trials on the same test day with recipient present (RP) and recipient absent (RA) trials alternating on consecutive test days.During RP conditions, both a donor and recipient were present, each in different compartments, whereas, during the RA condition, only the donor was present in compartment A. The RA condition served to test whether the prosocial response occurred specifically when a partner could benefit, and extinguished faster when there was no partner to benefit. All trials were recorded on video. Similar to Phase One, the apparatus was suspended in the back corner of the home cage, against the back wall of the home cage. Unlike in Phase One, the inner mesh door was closed and locked. On RP trials, one member of the dyad was placed in compartment A, while the other member of the dyad was placed in compartment B of the apparatus. The left compartment (compartment A) always contained the sensor and contained the donor. In order to escape the apparatus and rejoin the home group, a donor bat needed to assist a recipient bat by hanging in the correct area of the compartment to trip the sensor. By doing so, the donor bat was able to release the trap door in their recipient’s compartment, allowing the recipient to escape.On each trial during the RP condition, a single dyad was placed in the testing apparatus. Two trials per day were conducted with each dyad being tested for five minutes per trial. Trials always lasted five minutes even if the recipient bat was released. This was done to ensure that there was no incentive for the donor to release the trap door faster so that it could rejoin the colony to satisfy its own selfish motivations. If the donor bat failed to release the recipient bat by the end of the trial, a staff member manually released the bat(s) and returned them to the home group. During each trial a researcher would record, if the donor released the trap door for the recipient and the latency for the actor to release the trap door. The RA condition was identical in all respects to the RP condition, with the exception that there was no recipient bat in the adjacent chamber. During the RA condition, a researcher recorded if a bat released the trap door, and if so the researcher recorded the latency for the bat to release the trap door.After testing all dyads once, it was observed that some of the subjects were beginning to display signs of becoming unmotivated to move within the apparatus such as hanging motionless near the inner mesh door of the apparatus for a majority of the trial. To combat this, the dimensions of compartment B were shrunk by 8 cm on each side to provide a slightly more stressful state for the recipient, and to prevent the actor from hanging on the side of the mesh immediately adjacent to the recipient. Researchers examining prosocial behavior in rats using an escape paradigm  constraiBecause of the modification to the apparatus, we considered the test phase to consist of two distinct phases. Block one consisted of the trials that occurred before the modification took place, and Block two consisted of trials that occurred after the modification took place. Over the course of both time blocks and the entirety of testing, each bat was paired with each recipient once in each test block, once as donor and once as recipient. In the second block of testing, bat 346B was pregnant and gave birth to a pup. Bat 346B was then removed from the study so as not to add stress on her and the pup. To ensure that each bat was able to participate as a donor each test session, one bat was randomly selected each night to participate twice as a recipient to fill the gap left by her absence. Other than the modification of the apparatus and one bat being randomly assigned to participate twice as a recipient each test session, there were no procedural differences between the two blocks of testing. t-tests to compare the latency of release on RP and RA trials for only the subset of trials on which the trap door was released.Two repeated measures ANOVAs were conducted. The first assessed whether there was a significant difference in the average frequency of the bats to release the trap door of compartment B between time blocks and test conditions. The second assessed whether there was a significant difference in the average latency of the bats to release the trap door of compartment B between time blocks and test conditions. We predicted an interaction such that frequency of release should decline across blocks, but should decline more rapidly in the RA condition compared to the RP condition. Secondly, we predicted an interaction that latency of release should decline across blocks, but should decline more rapidly in the RP condition compared to the RA condition. Because there were several trials on which the bats did not release the trap door, we assigned a maximum trial length to the latency variable on these trials. However, this led to the data not being normally distributed. We thus also conducted paired-Two additional repeated measures ANOVAs were then performed in order to assess if bats as donors were more likely to assist any specific recipient bats compared to other recipient bats. First, a Repeated Measures ANOVA of each donor bats’ average frequency to release the trap door of compartment B for each recipient bat was conducted. Secondly, a Repeated Measures ANOVA of each donor bat’s average latency to release the trap door of compartment B for each recipient bat was conducted. To account for instances of missing data, as donors could not be paired with themselves as recipients, the total average frequency and total average latency of the donor to release compartment B was used in these cells. We predicted a main effect of recipient such that frequency and latency to release the trap door would vary as a function of the identity of the bats.Individual goodness of fit chi-square tests were conducted for each of the 12 subjects to determine if any individual bats behaved prosocially regardless of whether the overall effects were significant. Chi-square tests compared the percentage of trials in which each donor bat activated the sensor to release the trap door when a recipient bat was present and when a recipient bat was absent. We predicted that prosocially motivated bats would have a significantly higher percentage of trials in which they released the trap door when a recipient bat was present relative to when a recipient bat was absent. N = 248, r = 0.985, p < 0.001).An independent rater coded 47% of the trials . A Pearson correlation was conducted on the trials coded by the primary and secondary raters, and the coders showed strong agreement,  = 1.986, p = 0.186, η2 = 0.153, with an observed power of 0.251, but there was a significant effect of time block F = 5.069, p = 0.046, η2 = 0.315, with an observed power of 0.537. The bats generally released the trap door less often over time. In addition, the interaction between time and condition approached significance F = 3.771, p = 0.078, η2 = 0.255, with an observed power of 0.426. Two repeated measures ANOVAs were conducted to assess whether there was a significant difference in frequency of the bats to release the trap door of compartment B between time blocks and test conditions, and secondly a significant difference in latency of the bats to release the trap door of compartment B between time blocks and test conditions. Two factors were analyzed; test conditions , and time blocks . There was no significant effect of testing condition on frequency, There were no significant effects of condition or time on latency to release the door and no significant interaction.N = 12). The first ANOVA assessed the donor’s average frequency to release the trap door of compartment B for each recipient and revealed no significant effects of recipient F = 0.842, p = 0.378, η2 = 0.071 with an observed power of 0.134, suggesting that donor bats did not release the trap doors differentially depending upon the recipients. The second ANOVA assessed the donor’s average latency to release the trap door of compartment B for each recipient and also revealed no significant differences F = 0.002, p = 0.965, η2 = 0.000, with an observed power of 0.050 suggesting that donor bats did not release the trap doors at faster rates for any particular recipients. It is possible that with increased power and more trials with each recipient, an effect would have been more likely to be obtained.Two additional repeated measures ANOVAs were then performed in order to assess if donors were more likely to assist any of the other specific bats as a recipient. The within subjects factor was the identity of the paired recipient (N = 11). Only one test, for bat 1OE7, revealed a significant difference χ2 = 13.20, p = 0.001 between RP and RA conditions. Contrary to predictions, this bat released the trap door significantly more often on the RA (N = 17) than on RP (N = 4) trials. It should be noted that A2A7 also showed a trend approaching significance to release the trap door of the recipient’s compartment more on RA (N = 18) than RP (N = 11) trials; χ2 = 4.956, p = 0.055. Although bat COD3 released the trap door of compartment B more often in the RP (N = 16) condition than in the RA (N = 11) condition, this difference was not significant. Thus, contrary to predictions, none of the individual bats behaved in a manner consistent with prosocial motivations. In addition, we assessed whether any individual’s frequency to release the trap door of compartment B significantly differed between RP and RA conditions by conducting 12 individual Goodness of fit Chi-Square tests. t-test comparing latency to release on RP and RA trials including only trials on which the door was released. The difference approached significance, with bats showing a very small tendency to release the door more quickly when recipients were present (M = 99 s) compared to when they were absent (M = 114 s), t141 = −1.70, p = 0.09).Because of the number of trials in which a donor did not release the trap door, the latency data was not normally distributed when a maximum trial duration was assigned as the value for latency. There is not an appropriate transformation for such data. Thus, we opted to omit trials on which the trap door was not released and re-analyze this subset of data. When viewed using a histogram, these data did appear to be normally distributed. Thus, we also conducted a paired The results from this study indicated that, when bats were separated from the group and placed in two compartments where one bat could display prosocial behavior by releasing a conspecific, the bats failed to demonstrate evidence of prosocial motivations either at the group or individual level. Furthermore, although results indicated that, over time, the bats exhibited a sharper decline of release in the RA condition as hypothesized, the overall rate of release in the second block of testing was similar for RA and RP conditions. Thus, there is no clear evidence from this study that Jamaican fruit bat females are motivated to perform a behavior to release conspecifics that cannot release themselves.Bats 1OE7 and A2A7 released the trap door more often in the RA condition than in the RP condition. Although these two bats did release the trap door more often in the RA condition, when examining their individual data in To explain the difference in frequencies to release the trap door in the RA condition between time blocks and the observed trend, it is possible that reducing the size of compartment B in the second time block may have had an influence on the donor’s behavior. As the recipient rats in  displayeThe lack of finding a prosocial behavior in the Jamaican fruit bats is somewhat surprising but also consistent with other lab studies examining the presence of prosocial behavior in other non-human animals. Studies of prosocial behavior in chimpanzees have both failed to demonstrate evidence of prosocial behavior ,16 and pOur experiment hinged upon the comparison of release rates and latencies between RP and RA conditions. If the bats had behaved differently in these two conditions, we could have ruled out the possibility that they failed to associate their own behavior (moving near the sensor) and the outcome of releasing a conspecific. However, given the lack of main effect of test condition, it remains possible that bats did not form that association, thus making it impossible for them to reveal prosocial motivations.Another significant limitation of the current study is that each bat interacted with each recipient only twice throughout each phase of the experiment. More trials might be needed to detect interactions between individual actors and recipients. Another limitation was the size that compartment B could be reduced to during the second time block of testing. In , the recA final limitation that the current study encountered was successfully motivating the bats to participate during the data collection. A series of pilot procedures utilizing food as a motivational reward, to test for the presence of prosocial behavior in the bats indicated that the bats were uninterested in the food reward and unwilling to participate. As Jamaican fruit bats live in stable groups of several females with one or two males and spend a significant amount of time in close contact , the curIt is possible that prosocial behavior in bats is exhibited only by vampire bats. Vampire bats feed in a unique way and have to feed at regular intervals or they will expire . Due to It seems unlikely that vampire bats would be the only bat species to engage in prosocial behaviors, as fruit bats have been observed participating in cooperative behaviors in the wild, such as mate guarding  and pup In conclusion, the results from this study revealed that, donor bats decreased the behavior of releasing the trap door of compartment B in both conditions over time, with a slightly sharper decrease taking place in the RA condition, while continuing to release the trap door at a similar frequency over time when a recipient bat was present. However, one documented failure to find evidence of prosocial behavior in Jamaican fruit bats should not dissuade future research efforts. Researchers should examine other contexts that would motivate bats to participate in data collection, and include the recording and possible manipulation of distress calls. The current procedure and findings should help point the way to new directions for exploring this topic in this, and closely related species.


Dementia & Neuropsychologia was held. In this meeting wediscussed strategies to improve and increase the sourcing of manuscripts fromdifferent regions and countries, and to streamline the peer reviewing process. A fewnew procedures were suggested and approved, such as the establishment of junioreditors to help the senior editors in the first reading of the manuscripts plus theinclusion of a checklist to be filled out by the author(s) before submitting themanuscript.In July 2008, during the International Conference on Alzheimer Disease held inChicago, Illinois, the first meeting of the Advisory Editorial Board ofDementia & Neuropsychologia  as the official journal of the BrazilianAssociation of Geriatric Neuropsychiatry (BAGNP). The BAGNP was founded in 1996,with the remit of disseminating knowledge to improve the quality of life in elderlyimpaired by neuropsychiatric disorders. The BAGNP brings together physicians fromthe three medical specialties whence it derives its name, as well as clinicians,neuropsychologists, physiotherapists, occupational therapists, social assistants,speech therapists and gerontologists, and has being pivotal in developing proficientprofessionals in this multidisciplinary area in Brazil. The BAGNP has fosteredresearch and promoted the dissemination of results and conclusions of researchthrough symposia and conferences held nationwide.The most important and unanimously approved decision taken at the Advisory EditorialBoard meeting was to elect Dementia & Neuropsychologia,all members of BAGNP will be receiving the journal, as will members of theScientific Department of Cognitive Neurology and Ageing of the Brazilian Academy ofNeurology, allowing articles published in the journal to swiftly reach a largeproportion of the professional and research community involved in the areas ofinterest of Dementia & Neuropsychologia in Brazil.In this broadening of the readership of Dementia &Neuropsychologia is now available online. All articles published sinceits launch can now be easily accessed in pdf format on the Brazilian Academy ofNeurology site (abneuro.org), via a convenient home page link. This valuable supportby the Brazilian Academy of Neurology and its Scientific Department of CognitiveNeurology and Ageing is gratefully acknowledged.Another development to our readers and collaborators is that Dementia & Neuropsychologia wehave the satisfaction of publishing papers from two Argentinian research centers.This a strong vote of confidence of these collaborators from a neighboring countryin the potential of the journal as well as proof of their involvement in theendeavor to create a more open journal to expedite publication of high qualityresearch produced by our respective countries.Last but not least, in this issue of Ricardo NitriniEditor-in-Chief


Insecticide-treated bed nets (ITNs) have played an integral role in malaria reduction but how insecticide depletion and accumulating physical damage affect ITN performance is poorly understood. More accurate methods are needed to assess damage to bed nets so that they can be designed, deployed and replaced optimally.Anopheles gambiae in near approach (1–½ cm) to occupied untreated rectangular bed nets in a laboratory study were used to quantify the amount of mosquito activity (appearances over time) around different parts of the net, the per-appearance probability of a mosquito coming close to holes of different sizes (hole encounter) and the per-encounter probability of mosquitoes passing through holes of different sizes (hole passage).Video recordings of female Appearance frequency on different parts of the net reflected previously reported patterns: the area of the net under greatest mosquito pressure was the roof, followed by the bottom 30 cm of the sides, followed by the 30 cm area immediately above this, followed by the upper two-thirds of the sides. The ratio of activity in these areas was (respectively) 250:33:5:1. Per-appearance probability of hole encounter on all parts of the net was strongly predicted by a factor combining hole perimeter and area. Per-encounter probability of hole passage, in turn, was strongly predicted by hole width. For a given width, there was a 20% greater risk of passage through holes on the roof than holes on the sides.Appearance, encounter and passage predictors correspond to various mosquito behaviours that have previously been described and are combined into a prototype mosquito entry risk tool that predicts mosquito entry rates for nets with various amounts of damage. Scenarios that use the entry risk tool to test the recommendations of the WHOPES proportionate hole index (pHI) suggest that the pHI hole size categories and failure to account for hole location likely sometimes lead to incorrect conclusions about net serviceability that could be avoided by using an entry risk tool of the form presented here instead. Practical methods of collecting hole position, shape and size information for bed net assessments using the tool in the field are discussed and include using image analysis and on-line geometric analysis tools.The online version of this article (doi:10.1186/s12936-017-1951-4) contains supplementary material, which is available to authorized users. The hundreds of millions of insecticide-treated bed nets (ITNs) distributed in sub-Saharan Africa and other malaria endemic areas are acknowledged to have played a major role in the global malaria control and elimination effort which has reduced the total malaria burden by more than half since 2000 .An. gambiae, the lower third of the net sides  come under intermediate pressure and the upper two-thirds of the nets receive the least pressure. Sutcliffe and Yin  × [0.34ln (hole width) - 0.55]#passages/FA2h = [9.5 × 9 × 10−6 (perimeter + area)]  ×  [0.30ln (hole width) - 0.52]low: #passages/FA2h = [1.5 × 9 × 10−6 (perimeter + area) × [0.30ln (hole width) - 0.52]high: #passages/FA3h = [0.3 × 9 × 10−6 (perimeter + area)] × [0.30ln (hole width) - 0.52]#passages/Incorporating these values into the general model for entry risk (1) yields four FA-specific equations predicting the hourly net entries through holes in each:An. gambiae in Additional file As a demonstration, these equations are packaged into an Excel spreadsheet .Entry risk scores can be expressed for any time period by multiplying the values from the spreadsheet for hourly risk by the number of hours.Risk scores can be expressed for more than one mosquito attacking the net by multiplying the values from the tool by the number of attacking mosquitoes.Risk scores for any number of mosquitoes present outside the net for any number of hours can be calculated by combining the previous two measures.The maximum hole length or width that can meaningfully be entered in each row of the spreadsheet is 300 mm (30 cm) because all values in the model are based on sampling units of 30 cm × 30 cm. Holes longer than 300 mm can be handled by dividing them between rows in the spreadsheet. For example, a hole 400 mm long could be entered as two holes in successive lines, one with a length of, for instance, 250 mm and the other with a length of 150 mm.Portions of holes that span more than one FA should be apportioned accordingly and the components’ dimensions should be entered into the appropriate parts of the spreadsheet.Holes with a width of 5 mm or less are considered by the model to be impassable and will not affect the net score.Because people using bed nets in real-life are not likely to sleep just in the middle of the net or remain still and because nets often accommodate more than one person at a time, the model as presented takes a cautious approach by scoring all roof holes as though they are in FA1.Bed nets in normal use hang and sag in various ways not reflected by these idealized nets. This will likely affect mosquitoes as they fly along the net surfaces and interact with holes and damaged areas. Describing and accounting for these effects may lead to improvements to this tool but these are outside the scope of the present study,The tool assumes the net is a perfect sink for mosquitoes and does not attempt to account for mosquitoes leaving it.Because all experiments were done in still air which allowed the host odour plume to rise vertically (see Sutcliffe and Colborn ), the toAn. gambiae) release into a tent housing a fully-charged PermaNet 2.0 net. Based on flight activity reduction alone this would translate to an hourly entry risk reduction over the untreated net prediction of 70% or more since hole encounter and hole entry are both events that require the mosquito to be active. Despite insecticide effects, it has been shown experimentally that people sleeping under holed treated nets may be bitten by insecticide-susceptible mosquitoes even though the mosquitoes are eventually killed by net contact ..An. gambWhen using the spreadsheet, the following should be kept in mind2 on the roof (approximately 5 roof holes/net). Similarly, Vanden Eng et al. [The most striking aspect of the model is the great importance it ascribes to holes on the roof of the net. This is due, in part, to the much greater mosquito pressure on the roof and, in part, to the fact that roof holes of any size are approximately 20% more passable than holes of the same size on the net sides. Surprisingly little information is available in the literature on the size and detailed distribution of damage in used nets. This makes the real-life importance of roof holes difficult to evaluate. One study  reportedg et al.  reportedAn. gambiae. The modelled net has three large rectangular holes  in each of FA2-low and FA2-high and five such holes in FA3. It also has three smaller square holes (3 cm × 3 cm) on the roof. Assuming the external pressure on the net is one mosquito at all times, the total entry risk estimated by the model  . The pHIThe following paragraphs contrast results yielded by the pHI method and by the risk assessment tool proposed here.An. gambiae, the greatest pressure is on the net roof followed by the lower third of the net sides. This makes these the areas where mosquitoes are most likely to gain entry if they are damaged. Thus, it is critically important that information about the location of holes on the net is recorded and used in the net assessment. Unfortunately, the pHI field protocol discards this information after holes in the net have been counted [An. gambiae. For example, sticky square collections of the sort that helped define the FA map for An. gambiae, showed a different configuration for An. albimanus which appeared to be more strongly concentrated on the net roof and did not congregate near the bottom of the net [This study and others –13 demon counted . As a re the net . Given tIn the calculation of the WHOPES pHI, each hole over ½ cm is measured against a template which, whether the hole is round or not, is used to assign a diameter value to it. Hole ‘diameter’ is then used to assign each hole to one of four size classes . These aAn. gambiae entry risk represented by holes at the extremes of each of the size classes  where ‘a’ is half the length of the major axis and ‘b’ is half the length of the minor axis. This will work for any elliptical hole and for circular holes (for which the major and minor axis dimensions are the same) though as holes begin to approach roundness, it would be simpler and require less measurement to use the formula for area of a circle.If holes are elliptical, or can be approximated by an ellipse, the dimensions of the major and minor axes can be used to determine area using the formula 2pi ((a2 + b2/2)1/2) where ‘a’ is half the length of the major axis and ‘b’ is half the length of the minor axis and where the major axis is not more than three times the minor axis. Vanden Eng et al. [The perimeter of an ellipse is not as straightforward to calculate as, for instance, the perimeter of a circle or rectangle but can be approximated to within 5% by the formula g et al.  report tg et al.  that bouA hole, or any given part of a hole, must be over ½ cm wide for the tool to consider it passable; therefore, holes (or parts of holes) narrower than this need not be entered in the model . In the case of a circular hole, diameter serves adequately as its width but for elliptical or irregular holes, an average width is required. This means measuring hole width at intervals along its length and taking the average of all section widths wider than ½ cm. The more frequent the intervals, the more accurate the estimate will be but the more labour intensive the task will become. In the examples Figs. , 13, 14,In the absence of technological aids, hole dimensions would have to be estimated manually but since most net holes have fairly simple shapes this should be feasible. Measurements could be done in a manner similar to how hole size is currently determined for the WHOPES pHI. That is, with reference to a template held behind the hole as a guide . In thisThere are also several technological aids available that may reduce the labour intensiveness and increase the accuracy of this process. One such approach is with image analysis of digital photographs of the holes. Several suitable image analysis packages are available in which it would be possible to develop macros or plugins for measuring hole dimensions such as those discussed here. For example, ImageJ is freely available for download from the National Institutes of Health . A key pThere are also a number of graphing utilities that could be useful in this context. One such utility (available online or downloadable to use offline in the Chrome web browser) which allows the user to model ellipses of virtually any size, is available on the ‘Math Open Reference’ website . AnotherThis tool provides a way of estimating the rate of mosquito entry into a damaged net. Depending on how damage is distributed, the tool shows that a net with relatively little damage  may have a higher entry risk than a net with much more extensive damage . Thus, the tool should be an effective way of determining and comparing net protective value (net serviceability) but it would not necessarily reflect the overall physical condition (durability) of bed nets. The distinction between net serviceability and durability is important and each quality must be measured and applied appropriately. Clearly, the entry risk tool is appropriate for questions relating to net serviceability and the timing of net replacement once the nets are in place. On the other hand, the WHOPES pHI or the composite index  may be bTo understand better the determinants of mosquito entry into a damaged net and to improve—if needed—the weighting system for hole counts in the proportionate hole index, there is a need to study the relationship between hole size and position on an effective LLIN and the influence of total net size compared to the size of the hole.” The prototype tool presented here is the first of its kind based on direct observations and measurements of mosquito behaviour around bed nets and is a direct response to this stated need. While it may be possible to refine its accuracy for different mosquito species and specific circumstances, the tool even as presented here should provide more meaningful estimates of net protectiveness than anything else currently available. The estimates the tool provides may be used in various ways. Most simply, they could form the basis of a decision tree scheme similar to the way the pHI is currently used to establish thresholds of net ‘serviceability’. This would set ‘acceptable’ levels of mosquito entry into the net. While this would be expressed in meaningful terms (mosquito entry as opposed to simple damage), it would suffer from some of the same drawbacks as the pHI thresholds; namely, the thresholds would be arbitrary. In addition, this would only measure the risk of mosquitoes entering the net, not the risk of malaria per se. The tool’s ‘value added’ is that it provides specific estimates of the numbers of mosquitoes that will enter damaged nets making it a quantitative predictive tool. Accordingly, it may be possible to use the tool in combination with entomological and epidemiological data  to determine actual locale/epidemiological situation-specific malaria risk levels posed by worn nets. When used in this way, the amount and type of damage that defines a ‘failed net’ could be expressed in terms of malaria risk meaning that it is likely that a net that is judged as ‘failed’ in one epidemiological setting (e.g. where sporozoites rates are high) might be judged to be serviceable in another (e.g. where sporozoites rates are low). This would allow a nuanced approach to bed net assessment and replacement that could help maximize public health benefit while minimizing net replacement costs.In its report to the Malaria Policy Advisory Committee (MPAC) on issues relating to estimates of bed net survival in the field, the Vector Control Technical Expert Group (VCTEG)  lists se


Most real-world events stimulate multiple sensory modalities simultaneously. Usually, the stiffness of an object is perceived haptically. However, auditory signals also contain stiffness-related information, and people can form impressions of stiffness from the different impact sounds of metal, wood, or glass. To understand whether there is any interaction between auditory and haptic stiffness perception, and if so, whether the inferred material category is the most relevant auditory information, we conducted experiments using a force-feedback device and the modal synthesis method to present haptic stimuli and impact sound in accordance with participants’ actions, and to modulate low-level acoustic parameters, i.e., frequency and damping, without changing the inferred material categories of sound sources. We found that metal sounds consistently induced an impression of stiffer surfaces than did drum sounds in the audio-only condition, but participants haptically perceived surfaces with modulated metal sounds as significantly softer than the same surfaces with modulated drum sounds, which directly opposes the impression induced by these sounds alone. This result indicates that, although the inferred material category is strongly associated with audio-only stiffness perception, low-level acoustic parameters, especially damping, are more tightly integrated with haptic signals than the material category is. Frequency played an important role in both audio-only and audio-haptic conditions. Our study provides evidence that auditory information influences stiffness perception differently in unisensory and multisensory tasks. Furthermore, the data demonstrated that sounds with higher frequency and/or shorter decay time tended to be judged as stiffer, and contact sounds of stiff objects had no effect on the haptic perception of soft surfaces. We argue that the intrinsic physical relationship between object stiffness and acoustic parameters may be applied as prior knowledge to achieve robust estimation of stiffness in multisensory perception. Humans extract environmental properties from multiple sources of sensory information. Haptic sensations are usually accompanied by contact sounds. From the “ding” of a metal plate, the “clunk” of a wood table, or the “bong” of a drum, we can often tell what the object is made of, its size, how it is being struck, and even imagine the tactile sensation that occurs when striking it , 2. If wAlthough extensive studies have been performed to examine human abilities of deriving object attributes from impact sounds, such as inferring material composition –6, holloIn the field of virtual reality, researchers have noticed the influence of sound on the haptic stiffness sensation –17. The However, the interaction between auditory and somatosensory information is not yet fully understood. On the basis of anatomical, electrophysiological and neuropsychological evidence, it is reasonable to assume that the neural networks for auditory perception are organized hierarchically from spectrotemporal feature detection to more general object categorization . In bothWe conducted three experiments to estimate the influence of different auditory features on the haptic perception of surface stiffness. We first assessed whether the perceived stiffness would change along with the contact sounds of different material categories in audio-haptic tasks, where haptic stimuli at different stiffness levels were provided. Next, we adjusted the frequency and damping parameters while maintaining the category property in Experiments 2a and 2b, based on previous work in sound source perception , 24, to In this experiment, participants were asked to remember and reproduce the stiffness of standard surfaces with different contact sounds in an adjustment task.Eight right-handed participants : 38 ± 6 years) took part in Experiment 1. All participants had normal or corrected to normal vision, normal hearing, and reported no known motor deficits. The experiment was conducted according to the principles in the Declaration of Helsinki, and was approved by the ethics committee of the National Institute of Information and Communications Technology. All participants provided their written informed consent prior to the experiment.The haptic stimuli were presented with a force-feedback device, PHANToM Premium 1.5A , and were developed using Reachin API  which ran on a Dell Precision 690 computer with a 3.0-GHz Xeon processor and a Creative Sound Blaster X-FiTM audio card. The virtual surfaces that participants tapped with the stylus of the PHANToM were light gray squares that were used only to show the tapping area and did not change their appearance during the experiment. Participants wearing shutter glasses could see the virtual surfaces and a dark gray sphere representing the movement of the stylus through a mirror reflecting the three dimensional (3D) images rendered on a 17-inch CRT monitor  as shown in M = {f, d, a}, which describes a vibrating object by a bank of damped harmonic oscillators with modal frequencies fn, damping parameters dn, and amplitudes an, where the mode number n = 1, …, N [h(t).y(t) is generated as the convolution of the impulse response h(t) and the input force x(t), i.e.,The sound model in the physically motivated modal synthesis method is  1, …, N . The vibdrum, wood and metal. The parameters of modal frequencies were estimated by fitting them to the sampled sound file using the method described in our previous work [Impact sounds for sound model measurement were recorded in an anechoic room  so that ous work . For eacous work –31, becaParticipants were seated in front of the force-feedback device and looked into the mirror that reflected the monitor image. As shown in After finishing the task, we assessed informally the ability of participants to identify the material of the sound-generating objects. All of the participants could differentiate among the contact sounds and infer the three kinds of gross material categories: metal, wood, and drum (stretched leather) that could make the sounds, which were the same as the materials of the objects that we used to sample the sounds.1,7 = 98.643, p < .0001). Meanwhile, the sound condition turned out to be significant . However, no interaction effect existed between the surface stiffness and contact sound conditions . Multiple comparisons of the four sound conditions using the Ryan’s procedure showed that metal and wood sounds enhanced the feeling of a hard surface compared with the no-sound condition. No-sound–Metal = –93.376 N/m, p < .001; No-sound–Wood = −78.095 N/m, p = .002. The drum sound condition did not differ significantly from the no-sound condition (p = .284), but differed significantly from the metal (p = .005) and wood (p = .023) sound conditions.t-test results showed that the participants appeared to under-estimate the hard surface  and over-estimate the soft one  in the no-sound condition, which could be a tendency toward the mean stiffness level when their judgments were made under uncertainty. Therefore, we conducted Experiments 2a and 2b to address these issues.Experiment 1 indicated that the contact sounds of different material categories had different effects on haptic stiffness perception. Surfaces with wood and metal sounds were perceived as harder than no-sound surfaces, while surfaces with the drum sound were not significantly different from no-sound surfaces. Nevertheless, it is too early to conclude that the material category is the main factor in auditory information that influenced haptic stiffness perception. First, the categories in Experiment 1 were very different from one another, but at the same time the acoustic parameters  of different materials were also distinct from each other. It is not clear which factor actually influenced the results of stiffness adjustment, the material category or the lower-level acoustic features. Second, because the participants adjusted the stiffness of a test surface after the exposure to the standard stimulus had ceased, the adjustment task was strongly memory-dependent, which would have reinforced the role of categorical processing. Furthermore, the participants could not precisely reproduce the stiffness of standard surfaces even in the no-sound condition. Our The first modification from Experiment 1 was that lower-level acoustic parameters were modulated to examine which factor, material category or acoustic parameters, was more relevant to the audio-haptic integration process. The second modification was that we used two approaches to reduce the memory-dependency of the adjustment task in Experiment 1. Experiment 2a still applied the stiffness adjustment task, but the standard surface and the test surface were presented side by side so that participants could tap both of them during the adjustment procedure. Experiment 2b was a stiffness comparison task, in which the participants made their judgment immediately after their exposure to the two stimuli and did not have to remember the haptic feeling of the standard surfaces for a long time. We conducted both of them to test whether different experimental procedures would influence the results. In each experiment, the audio-only tasks were also carried out to examine whether auditory information has the same effects in unisensory and multisensory tasks of stiffness perception.There were 10 participants  in Experiment 2a. Another nine participants  were recruited for Experiment 2b. All participants were right-handed, and had normal or corrected to normal vision, normal hearing, and reported no known motor deficits. They were paid for their participation and were naïve to the purpose of the experiment. All experiments were conducted in accordance with the recommendations of the ethics committee of the National Institute of Information and Communications Technology with written informed consent from all participants. All 19 participants provided their written informed consent in accordance with the Declaration of Helsinki prior to the experiments.d / (πf), d: damping, f: frequency) was proposed to account for the identification of gross material categories. If we modified  to  by adjusting the centroids of their mode frequencies fc  constant, then the modified sounds  as shown in Eqs fcM1 = 2430.73 Hz, fcD1 = 283.56 Hz. We chose the coefficients as α = 0.21, β = 1.80.We chose metal and drum sounds whose mode frequencies were wide apart for Experiments 2a and 2b. The mode parameters of the two original sounds , which were the metal (M) and drum (D) sounds used for Experiment 1, are listed in the table in ncies fc  to be thThe distribution of modes of the original and modified sounds in the frequency-damping space is shown in CR) from 1 (low) to 7 (high) points. The categories to choose from were drum (A), metal (B), or others (C). When participants chose category C, they could write down the material such as glass, wood, plastic, and so on if they had one in mind. There were 50 types of sound, half of which were modified from the original metal sound M1 and the other half from the original drum sound D1. We manipulated the mode frequency fn and damping parameters dn of sound models i and j as shown in Eqs li,ki} were values from {0.1, 0.21, 0.5, 1.0, 1.2}, and the elements of coefficient set {lj, kj} were values from {0.6, 1.0, 1.4, 1.8, 2.2}. So, when {li,ki} was {1.0, 1.0}, the sound was the original metal sound M1. When {li,ki} was {0.21, 0.21}, the corresponding sound was sound M2 used in Experiments 2a and 2b. When {lj, kj} was {1.0, 1.0}, the sound was the original drum sound D1 and {1.8, 1.8} produced the sound D2 used in Experiments 2a and 2b. The score of each sound was the sum of the confidence rating of each category  across all participants. There was one trial for each of the 50 sounds, which was presented in pseudo-randomized order, and no time limit was set for response.This was an audio-only task where the experimenter tapped virtual surfaces to produce different impact sounds, and participants wearing headphones chose the material category of the sounding-source and reported their confidence rating . By combining these sound conditions with two different surface stiffness levels , we produced eight kinds of standard surfaces . Typical examples of the impact sounds that resulted from tapping these surfaces are available in the Supporting Information online. This task consisted of eight trials for each of the eight experimental conditions presented in pseudo-randomized order. The audio-haptic session took 28.14 min on average, with standard deviation 10.33 min, while the audio-only session took 25.00 min on average, with standard deviation 8.50 min. For each participant, the mean value of the eight trials of each condition was taken as a sample for statistical analysis. Three-way repeated measures analyses of variance (RM-ANOVA) were performed with surface stiffness, material category, and acoustic parameter as factors.In Experiment 2b, we applied Scheffé’s paired comparison method  for bothSurface 1 is much harder than Surface 2: 2Surface 1 is slightly harder than Surface 2: 1Surface 1 is as hard as Surface 2: 0Surface 2 is slightly harder than Surface 1: −1Surface 2 is much harder than Surface 1: −2Each of the possible pairs of the 12 experimental conditions (including order of presentation) was presented once, in random order, for a total of 132 trials. The two values of the same pair with a different order of presentation were averaged, and the results were analyzed using the Nakaya variation of Scheffé’s paired comparison . The audIn Experiment 1, three factors could account for the result that the surfaces with sound M1 were perceived as stiffer than the surfaces with sound D1: (1) M1 is a metal sound, whereas D1 is a drum sound; (2) M1’s frequency is higher than that of D1; (3) M1’s damping parameter is larger than that of D1. According to the relationship among the acoustic parameters in the four sounds, for Experiments 2a and 2b we could make three different predictions under the assumption that the material category, frequency or damping parameters was the main factor, respectively: The order of surface stiffness should be M1 = M2 > D2 = D1 if the material category dominated the multisensory integration; however, if a lower-level feature were more important, because the spectral centroid of M2 and D2 were almost the same, the order would be M1 > M2 = D2 > D1 when frequency played the main role, and D2 > M1 > D1 > M2 when damping parameter was the key factor. It was also possible that Experiments 2a and 2b would yield different results if the audio-haptic integration strategy changed across tasks.li,ki} and {lj,kj} in Eqs ,,li,ki} 1,9 = 8.228, p = .0185), which shows that the metal sounds always gave an impression of a stiffer surface than the drum sounds in the audio-only condition. There was no main effect of surface stiffness  and acoustic parameter . Participants did not imagine the stiffness of sounds made by tapping hard surfaces differently from that of sounds made by tapping soft surfaces. In the paired comparison task . The yardstick Y.05 was 0.1585, which means that if the difference of preference values exceeds 0.1585, the two ratings differ significantly (p < .05). The preference order of stiffness was M1 > M2 > D2 > D1. Again, in this audio-only task, both the original and the modified metal sounds were perceived as stiffer than the drum sounds.The results of the audio-only conditions of Experiments 2a and 2b shown in son task , sounds 1, 9 = 75.959, p < .0001. In the paired comparison task, surfaces of different stiffness levels  were significantly different in their preference levels, shown in 11,440 = 368.65). The yardstick Y.05 was 0.1674.It seemed reasonable to assume that the above effect of sound on stiffness perception in audio-only conditions would also appear in audio-haptic tasks together with a significant effect of the stiffness level of surfaces. Indeed, the effect of the stiffness level was supported by our data. In the audio-haptic stiffness adjustment task (Experiment 2a), the adjusted stiffness of the test surfaces was significantly different for the two kinds of stiffness level (700 N/m and 200 N/m), despite the presence of contact sounds , F1, 9 =1,9 = 1.175, p = .223) and acoustic parameter  were not statistically significant, but there were interaction effects between the material and acoustic parameter factors, F1,9 = 23.810, p = .0009, and among the stiffness, material and acoustic parameter factors, F1,9 = 26.268, p = 0.0006. Post-hoc analysis using Ryan’s procedure showed that in the hard surface condition, surfaces with the original metal sound M1 were perceived as much stiffer than those with the original drum sound D1 , which was consistent with the results of Experiment 1 and the audio-only conditions. Further, the surfaces with M1 elicited a significantly stiffer impression than did those with the modified metal sound M2 . Meanwhile, the surfaces with the modified drum sound D2 also produced a significantly stiffer impression than did those with the original drum sound D1 . However, comparing the data of M2 with those of D2, we found that surfaces with the modified metal sound M2 elicited a significantly softer impression than did those with the modified drum sound D2 . This was opposite to the results of the audio-only adjustment task. None of these effects in the hard surface conditions were found in the corresponding soft surface conditions.To our surprise, we found that the effects of sound were not the same in the audio-haptic and audio-only tasks. In the audio-haptic adjustment task, the main effects of material category , we averaged our data across all of the haptic stiffness levels for the four sounds  in each experiment. As can be seen in Consistent with this observation, the Pearson product-moment correlation coefficients between the experimental results and each feature-based prediction see  indicateThe present results demonstrated that haptic stiffness perception was susceptible to auditory information, but the effect of contact sounds in the audio-haptic condition was not the same as that in the audio-only condition. These findings suggest that auditory features are used differently in unisensory and multisensory processes for stiffness judgment. We found two characteristics of audio-haptic stiffness perception, which are distinct from that of audio-only stiffness perception.First, the magnitude of the auditory effect on multisensory stiffness perception varied with the haptic stiffness level. In the audio-haptic tasks of Experiments 2a and 2b, the auditory effect was not significant when the sounds of stiff objects were presented with soft surfaces (200 N/m). One may suppose that sound amplitude accounts for this, because the synthesized sound was calculated from the convolution of contact force and sound models. A softer surface would generate a weaker force than would a hard surface, so that the amplitude of its contact sound was lower, and in turn the effect of sound would be less obvious. However, the data of the audio-only condition showed that the sounds made by soft surfaces (200 N/m) were not perceived significantly differently from those made by hard surfaces (700 N/m). Furthermore, in the medium stiffness condition (450 N/m) of Experiment 2b, the auditory effect was as strong as that in the high stiffness condition (700 N/m). This indicates that the variation of the auditory effect was not consistent with the change in sound amplitude, and the two are probably not causally related. A plausible explanation might be that the force feedback of soft surfaces (200 N/m) and the contact sound of stiff objects were not taken as signals generated by the same event, so the discrepant multisensory signals were not integrated effectively. As suggested in the studies of depth cue combination  and visuSecond, not all of the auditory features could be tightly integrated into the haptic processing flow. In our audio-only tasks, either the original or the modified metal sounds were judged to be stiffer than the original or modified drum sounds. Because stiffness is closely related to the material composition of an object, material category might be the most reliable property extracted from the auditory signal when participants are asked to estimate or judge stiffness from sound. This is in line with observations by Giordano et al. , i.e., tThe difference between the unisensory and multisensory results suggests that the low-level acoustic features, other than the material category, were deeply involved in the haptic processing flow, and the audio-haptic interaction might originate in a mandatory perceptual process , becauseWe also note that in the same material category, sounds with higher frequency and/or shorter decay time (large damping parameter) gave an impression of stiffer objects, which was common in the audio-only and audio-haptic conditions. The concordance among participants in associating the somatosensory perception of stiffness with acoustic features seems to reflect the regularities of the physical environment. In our tasks, the quantity defining stiffness was the restoring force that participants could feel from the stylus when tapping a virtual surface. Physically, the restoring force is mainly determined by material stiffness and tension, which are also two important factors for vibration and impact sounds. The stiffness or tension of a plate and a membrane is in direct proportion to the square of the object’s vibration frequency , i.e., sAlthough stiffness and acoustic parameters are closely related, object structure also causes a change in acoustic parameters, e.g., a longer bar produces impact sounds with lower frequency. Therefore, the estimation of object stiffness cannot be uniquely determined by acoustic parameters. Since humans can infer structural properties from contact sounds, such as size , 46, 47,S1 File(WAV)Click here for additional data file.S2 File(WAV)Click here for additional data file.S3 File(WAV)Click here for additional data file.S4 File(WAV)Click here for additional data file.S5 File(WAV)Click here for additional data file.S6 File(WAV)Click here for additional data file.S7 File(WAV)Click here for additional data file.S8 File(WAV)Click here for additional data file.S9 File(XLSX)Click here for additional data file.S10 File(XLSX)Click here for additional data file.S11 File(XLSX)Click here for additional data file.S12 File(XLSX)Click here for additional data file.S1 Table(DOC)Click here for additional data file.


Gelatinization of wheat and maize starch was complete at a temperature higher than Tc in the profile, which we define as the end temperature (Te). We propose that Te would be better to define the completion point of starch gelatinization than Tc.A thorough understanding of starch gelatinization is needed to control starch functional properties for food processing and human nutrition. Here, we reveal the mechanism of structural disassembly of rice, maize and wheat starch granules during thermal transitions in which a Rapid Visco Analyzer (RVA) was used to pre-heat the starches to certain transition points in the differential scanning calorimetry (DSC) heating profiles. This was done to generate sufficient material for structural analyses. The results from DSC, Raman, X-ray diffraction and scanning electron microscopy (SEM) analyses all showed that at the conclusion temperature (T Starch granules have a complicated hierarchical structure, which is examined at nano to micrometer scales covering: glucosyl links (0.3 to 0.5 nm); alternating crystalline and amorphous lamellae with a periodicity of 9–11 nm; the “blocklet” structures (20 to 500 nm); the semicrystalline growth rings (120 to 550 nm); intact granules (1 to 100 μm)5. The multi-scale structures of granules and the changes they undergo during processing and storage are the major determinants of starch functionality and food quality7.Starch is a major component in many foods and the main glycemic carbohydrate in the human diet. Native starch granules are insoluble in cold water and are made up of two α-glucans: lightly branched amylose and highly branched amylopectin. Amylose is proposed to be more concentrated at the periphery11. Many features of gelatinization are incompletely understood, especially the nature of structural changes that give rise to the endo- and exotherms observed in Differential Scanning Calorimetry (DSC) thermograms. DSC is the most widely applied technique to measure heat of gelatinization of starches, but comparisons between studies on the extent of phase transitions are sometimes difficult due to considerable variations in experimental conditions, especially water content, heating rate and time13.When an aqueous suspension of starch granules is heated above a certain temperature, the granules undergo an irreversible order-disorder transition known as gelatinization. Despite extensive studies, there is still a lack of a clear and agreed understanding of the mechanism of this transition14. Recent studies using DSC and swelling power measurements show that at water:starch ratios of 2:1 or greater, the gelatinization endotherm represents partial swelling of starch granules rather than full gelatinization16. To further understand the nature of the gelatinization endotherm obtained at high water contents, pre-heated wheat starch at different stages of gelatinization was generated using the Rapid Visco Analyzer (RVA) to simulate the DSC heating profiles17. The multi-scale structures of these gelatinized starches were characterized by a combination of XRD, Fourier Transform Infrared Spectroscopy (FTIR), Raman, SAXS, DSC and scanning electron microscopy (SEM), which showed that over a wide range of water content (0.5:1~4:1), considerable long- and short-range molecular orders remain at the conclusion temperature  of the starch gelatinization endotherm18.A combination of DSC with X-ray Diffraction (XRD) and Small Angle X-ray Scattering (SAXS) has shown that residual crystallinity and lamellar organization remain at the completion of the gelatinization endotherm18. These studies showed that heating starch in the RVA without stirring in different water:starch mixtures of ratios ranging from 0.5 to 4.0 brought about a loss of granular organisation, although some long- and short-range structural order remained at the equivalent of the DSC conclusion temperature. These results, which are consistent with starch gelatinization having been shown to be incomplete after the DSC heating profile16, indicate the RVA is useful to model the DSC thermal transitions and provide new insights into structural changes that occur during gelatinisation and retrogradation of starch. In the present study, maize, rice and wheat starches were used to further reveal the nature of DSC endothermic transitions over a wide range of starch:water ratios with the aid of RVA heating. The multi-scale structural changes in these starches were examined after heating in the RVA to Tc and to a higher temperature (Te), which is defined as the point when the DSC trace reaches a flat baseline16, as shown in Fig. c and Te of DSC endotherm. Our results suggested that Te would be better than Tc to define the point of completion of starch gelatinization.In previous studies, an RVA instrument was used without stirring as a model system to mimic the DSC heating profile and prepare sufficient materials to analyse starch structural changes during gelatinisation and retrogradation18. The amylose contents of wheat and rice starches were 25.0 and 11.5%, respectively, as determined according to the method of Chrastil19. Non-waxy maize starch (27.0% amylose), and amylose (A0512) and amylopectin (A8515) from potato starch, were purchased from Sigma Chemical Co. . All other chemical reagents were all of analytical grades.Starch was isolated from wheat and rice grains as described previouslyo), peak (Tp), conclusion (Tc) temperatures and enthalpy change (ΔH)) were determined from the data recording software. The end temperature (Te), as defined in the introduction, was also determined  equipped with a thermal analysis data station. Approximately 3.0 mg of native starches were weighed accurately into an aluminum crucible. Distilled water was added to obtain water:starch ratios (v/w) of 0.5, 0.75, 1.0, 1.5, 2.0, 3.0 and 4.0 in the DSC pans, corresponding to water contents of 33.3, 42.9, 50.0, 60.0, 66.7, 75.0 and 80.0%, respectively. The sealed pans were allowed to stand at room temperature for 4 h before analysis. The pans were heated from 20 to 95 °C at a rate of 10 °C/ min. An empty aluminum pan was used as the reference. For the freeze-dried starch samples obtained from RVA, a ratio of water:starch of 5:1 (v/w) was used in the DSC measurements. The thermal transition parameters . The mixtures in the canister were allowed to stand for 4 h at room temperature before heating. Rice and maize starch-water suspensions were heated separately in the RVA canisters without stirring to To − 10, To, Tp, Tc, Tc + 10, 95 °C. Wheat and maize starch-water suspensions were also heated to Te  of the band at 480 cm−1 is used to characterize the short-range molecular order of starch samples21.Raman spectra of freeze-dried starch samples were measured using a Renishaw Invia confocal Raman microscope system . A 785 nm green diode laser source was used in this study. Spectra were obtained from at least five different positions of each sample in the range of 3200–100 cmα source (λ = 0.154 nm) running at 40 kV and 30 mA. All starch samples were stored for one week in a desiccator over a saturated NaCl solution before measurements. The samples were scanned from 4° to 35° (2θ) at a speed of 2°/min and a step size of 0.02°. The relative crystallinity was quantified as the ratio of the crystalline area to the total area between 4° and 35° (2θ) using the Origin software .X-ray diffractograms were obtained using an X-ray diffractometer  with a Cu-KThe morphology of starch samples was observed using a scanning electron microscope . Freeze-dried starch samples were mounted on a stub using double-sided adhesive tapes, sputter coated with gold in a sputter coater . The imaging was performed at an accelerating voltage of 10 kV.Results are reported as the means and standard deviations of at least duplicate measurements. Analyses of variance (ANOVA) by Duncan’s test (p < 0.05) were conducted using the SPSS 19.0 Statistical Software Program .c of maize starch increased from 72.5 to 77.2 °C as water:starch ratio increased from 0.5 to 2.0, above which it increased slightly. The effect of water content on thermal properties of starch has been studied extensively; the extent of crystallite melting is limited at low water content but increases with increasing water availability22. From this and other previous studies, it can be seen that the source of starch is a major determinant of its thermal properties23.The thermal transition parameters of rice and maize starches are presented in Table c + 10 or 95 °C. With water-starch mixtures of 2:1, 3:1 and 4:1, endothermic DSC transitions were observed for maize starch after pre-heating to Tc in the RVA, but none were observed for rice starch. The values of Tc for rice and maize starches increased as the temperature of RVA pre-heating was increased  reflects primarily the disruption of long-range starch crystallinity during DSC heatinging Fig. , indicat−1 of all rice . The full width at half maximum (FWHM) of the band at 480 cmice Fig.  and maizice Fig.  increasec, the relative crystallinity was very low (about 5%), indicating that at Tc of the gelatinization endotherm, crystallites in rice starch were essentially all disrupted. However, with all maize starch samples after pre-heating to Tc in the RVA, the typical A-type X-ray diffraction patterns could still be observed, with residual crystallinity of 9 to 21%  of the DSC endothermic transition obtained when the ratio of water:starch was 2:1 or greater. Further disruption of structural order occurred on heating beyond Tc. This observation was consistent with the previous findings that residual crystallinity still remained at Tc17. Hence, we can conclude that gelatinization of maize and wheat starches did not commence at To and was not complete at Tc of the gelatinization endotherm. After pre-heating to Te, the multiscale structures of wheat and maize starches were completely disrupted, indicating a complete gelatinization at Te. Hence, we propose that Te of starch gelatinization endotherm can be defined as conclusion temperature of DSC gelatinization endotherm.Structural disruption of rice starch commenced at the onset temperature of the gelatinization endotherm and was essentially completed at the conclusion temperature. During the gelatinization endotherm, multiscale structures in rice starch were disrupted progressively with increasing temperature from T25. At low to intermediate water content , the gelatinization endotherm represents limited swelling of starch granules rather than full gelatinization of starches17. In water:starch mixtures of 2:1 or higher, the gelatinization endotherm determined by DSC has been considered to represent complete gelatinization of starches since pioneering worked of Donovan22. However, more recent studies have provided evidence that even at a high water content in water:starch mixtures (>67% or water:starch ratio of 2:1), the gelatinization endotherm of wheat and pea starches does not represent full gelatinization26.The mechanism of the molecular disassembly of starch granules during DSC thermal transition has attracted much research attentionc, indicating the endotherm represents the complete gelatinization. However at Tc, maize and wheat starches still had appreciable amounts of long- and short-range structural order, indicative of incomplete gelatinization. Further disruption of ordered structures occurred when wheat and maize starches were heated beyond Tc to Te, the temperature at which the DSC trace reaches a flat baseline. Hence, we conclude that gelatinization of wheat and maize starches starts after To and is more complete at Te in water:starch mixtures of 2:1 or greater. As starch gelatinization properties are often measured in such mixtures, we propose that Te can be defined as the end point of complete gelatinization.In the present study, the extent of structural changes in starch at temperatures corresponding to key transition points in the DSC endotherm was investigated. To this end, an RVA was used without stirring to mimic DSC heating profiles and obtain sufficient amounts of starch for subsequent analyses by DSC, Raman, XRD and SEM. For rice starch, there was essentially no residual structural order remaining in water-starch mixtures of 2:1 to 4:1 after heating to T


The SD had the opposite effects to the MD in all cases. The results suggest that soil drought more severely affects amylose synthesis than amylopectin synthesis in wheat grains, and moderate soil-drought improves molecular structure and functional properties of the starch.Drought influences cereal crop yield and quality. However, little is known about changes in the structural and functional properties of wheat starch under soil drought conditions. In this study, two wheat cultivars were subjected to well-watered (WW), moderate soil-drought (MD), and severe soil-drought (SD) from 7 tillers in the main stem to maturity. The structural and functional properties of the resultant endosperm starch were investigated. In comparison with WW soil, the MD increased starch accumulation in grains, the proportion of large starch granules, amylose and amylopectin long branch chain contents, and average amylopectin branch chain length, which were accompanied by the increase in activities of granule bound starch synthase and soluble starch synthase. MD treated-starch had a lower gelatinization enthalpy, and swelling power, but a higher gelatinization temperature, retrogradation enthalpy, and retrogradation percentage when compared to WW conditions. The MD also increased starch resistance to acid hydrolysis, amylase hydrolysis, and  Triticum aestivum L.) is an important cereal crop and a staple food for humans and animals worldwide. Starch is the major storage compound in wheat endosperm, accounting for 65–75% of the final dry weight of a grain, and is synthesized in the amyloplast of endosperm cells since 4 days after anthesis (DAA) and the endosperm structure no longer changes after 33 DAA2. Starch is mainly composed of linear amylose and highly branched amylopectin, which assemble to form a semicrystalline granule3. For linear amylose, the glucose units are joined through α--glycosidic linkages which are mainly catalyzed and elongated by granule-bound starch synthase (GBSS). Amylopectin mainly consists of long chains of α--linked D-glucopyranosyl units with occasional branching α--linkages that form branched structure. The α--glycosidic linkages are catalyzed and elongated by starch branching enzymes (SBE) and soluble starch synthase (SSS), respectively4. Amylose content and amylopectin fine structure greatly influence physicochemical properties that affect grain quality, flour quality, and starch properties4. Grain quality is a complex trait with various determinants, including physical appearance, nutritional value, and eating and cooking quality. These factors are important for consumers, and are associated with the physicochemical properties of crop starch, including hydration, gelatinization, volume expansion, and digestion properties6. Furthermore, apparent amylose content, pasting viscosity characteristics, gel texture, thermal and retrogradation properties, and amylose and amylopectin fine structures have been established to precisely evaluate the quality of grain and starch-based foods8.Wheat  to investigate how soil drought affects physicochemical properties of starches in wheat kernels, (2) to elucidate the relationship between functional properties and fine structures of starch, and (3) to test the hypothesis that moderate soil drought increases grain quality by improving structural and functional properties of starch in the kernel.Previous studies have mainly focused on the effects of drought on grain developmentThe leaf water potential (LWP) gradually decreased during the growing season Fig. . For plaSoil drought greatly affected grain weight and grain yield of both cultivars Figs  and 2a–hChanges in activities of soluble starch synthase (SSS) and granule bound starch synthase (GBSS) in grains under various soil moisture treatments during grain filling were shown in Fig. Compared with that in the WW grains, the total starch accumulation was significantly higher in MD-treated grains and markedly lower in SD-treated grains Table . The proThe apparent amylose content and molecular weight distributions of wheat starches showed variable responses to the soil moisture treatments Table . The MD 26. The weight distributions of the short amylopectin branch chain, long amylopectin branch chain, and amylose were calculated from the areas under peaks 1, 2, and 3, respectively. The extent of amylopectin branching is described by the area ratio of peaks 1 and 2, where a higher ratio indicates a higher degree of branching27. The peak areas of wheat starches in GPC are summarized in Table 28.In the gel permeation chromatography (GPC) profile of debranched starch, peaks 1 and 2 were designated as different amylopectin branch chains, consisting of short (A and short B chains) and long (long B chains) branch chains, respectively, while peak 3 corresponded with amylose29. The chromatograms of starches from different soil moisture treatments showed distinct differences. The percentages of A, B1, B2, and B3 + chains and the amylopectin average chain length in the starches are shown in Table The amylopectin chain length distribution using high-performance anion-exchange chromatography (HPAEC) is shown in Fig. The starch X-ray powder diffraction (XRD) patterns were similar among various soil moisture treatments, and exhibited typical A-type spectra with major diffraction peaks at approximately 15, 17, 18, and 23° (2θ), in agreement with the XRD patterns of normal cereal starches  and ∆Hgel, respectively. Compared with that of the WW treatment, the MD-treated starch had a higher gelatinization temperature, increased gelatinization range (∆T), and lower gelatinization enthalpy (∆Hgel). The SD treatment had the opposite effects. Furthermore, starch retrogradation was observed when the gelatinized samples were stored at 4 °C for 7 days (Table ret) and retrogradation percentage (%R) were significantly increased under the MD, while decreased under the SD treatment, for both cultivars and in both years  order in starch can be reflected by its gelatinization temperatures (TAspergillus niger amyloglucosidase (AAG), the two wheat cultivars exhibited relatively rapid initial rates from 0 to 8 h, and progressively decreased after 8 h  and lower levels of rapidly digestible starch (RDS) than gelatinized and retrograded starches, whereas retrograded starches had higher levels of slowly digestible starch (SDS) and lower levels of RDS than gelatinized starches, regardless of soil moisture levels Table . Starchegel, degree of hydrolysis , and RDS content , but positively correlated with ∆Hret, %R, and RS content  , and RDS content , and were negatively correlated with ∆Hret, %R, and RS content  , average amylopectin chain length, and proportion of large granules had negative correlations with relative crystallinity, swelling power, ∆H33. We also observed that severe soil-drought decreased wheat grain yield , soluble starch synthase (SSS) and ADP-glucose pyrophosphorylase (ADGP-ppase)21. In this study, we observed that, compared with the WW treatment, both GBSS and SSS activities in wheat grains were significantly enhanced by the MD treatment, but significantly decreased by the SD treatment during the grain filling period for both cultivars in both years  and small granules (diameter < 10.0 μm)35. Large and small starch granules exhibit different physicochemical properties due to their different chemical compositions 36. Previous studies have shown that soil drought reduced the granule size in cereal crops38. However, our results showed that the response of wheat starch granule size to soil drought varied with the degree of water stress , average amylopectin chain length, and proportion of large granules  was negatively correlated with amylose content and proportion of large granules  and Aspergillus niger amyloglucosidase (AAG) is usually used to simulate the effects of hydrolysis in the small intestine and subsequent glycaemic responses56. RDS causes a rapid increase in blood glucose level after ingestion, whereas SDS releases glucose slowly and consistently over an extended time. RS which resists enzymatic hydrolysis is fermented in the large intestine releasing short chain fatty acids which are considered health benefits57. Inter- and intra-molecular hydrogen bonds in the starch chains can be disrupted when starch granules in water are exposed to heat, allowing the granules to swell and disintegrate. Therefore, the availability of starch chains to digestive enzymes increases during gelatinization. During the retrogradation of gelatinized starch, amylopectin recrystallizes to form crystallites, while amylose chains associate to form an amorphous matrix, which increases resistance to digestive enzymes. We found that the SD-treated starch, which showed the lowest amylose content, had the lowest RS content, while the MD-treated starch, which showed the highest amylose content, had the highest RS content , and the average amylopectin branch chain length, but decrease the amylopectin short branch chain content, and the degree of amylopectin branching. The MD treatment exhibited a lower gelatinization enthalpy, pasting viscosity, and swelling power, but a higher gelatinization temperature, retrogradation enthalpy, retrogradation percentage, pasting peak time, and pasting temperature. The MD treatment could also increase starch resistance to acid and amylase hydrolysis. Gelatinized and retrograded starches synthesized under MD conditions had lower RDS content and higher RS content. The SD treatment showed opposite effects. Substantial enhancement in GBSS activity under the MD enhanced amylose synthesis, whereas substantial reduction in GBSS activity under the SD decreased it. Amylose synthesis was more sensitive to soil drought than amylopectin synthesis in wheat grains. Moderate soil-drought, i.e., midday leaf water potential was above −1.5 MPa and plant could rehydrate overnight, could improve starch molecular structure and functional properties in the grain.−1 organic matter, 105 mg kg−1 alkali hydrolysable N, 34.2 mg kg−1 Olsen-phosphorus and 68.0 mg kg−1 exchangeable potassium. On the day of sowing (3 November), 1 g N as urea and 0.2 g P as single superphosphate were mixed into the soil in each pot. N as urea was also top-dressed into each pot at the rate of 0.4 g and 0.6 g at 30 days after sowing (DAS) and 112 DAS, respectively. At the 3 leaves unfolded stage (2-digit code: 13)59, the plants were thinned to eight plants per pot . The plants were watered daily by hand to maintain a soil water content close to field capacity (soil moisture content 0.189 g g−1) until the stage at 7 tillers in the main stem (2-digit code: 27)59 when soil drought treatments were initiated. The flowering dates of both cultivars were similar, with initial flowering dates of April 9–10 in 2014, and April 11–13 in 2015. The air temperature during the grain filling period (April–May) in both study years was measured at a weather station close to the experimental site, and was shown in Supplementary Fig. The experiment was conducted at a research farm of Yangzhou University, Jiangsu Province, China  during two wheat growing seasons, November 2013–June 2014 and November 2014–June 2015. Two cultivars currently used in local production, Yangmai 16 and Zhenmai 9, with the protein content of 14.2% and 12.5%, respectively , were grown in porcelain pots, with twenty seeds per pot. Each porcelain pot  was filled with 18 kg sandy loam soil  that contained 20.2 g kg59, three levels of soil water potential (ψsoil) were imposed on plants by controlling water application. The Well-watered (WW) treatment was maintained at −20 ± 5 kPa , while the moderate soil-drought (MD) treatment was maintained at −40 ± 5 kPa (soil moisture content. 0.119 g g−1) and the severe soil-drought (SD) treatment was kept at −60 ± 5 kPa (soil moisture content. 0.091 g g−1). The soil water potential was monitored at a soil depth of 15–20 cm. A tension meter consisting of a 5-cm-long sensor  was installed in each pot to monitor soil moisture. Tension meter readings were recorded every 4 h between 06:00 and 18:00, daily. When readings dropped to designated values, 150, 120, or 90 mL of tap water were added to WW, MD, and SD plants, respectively. The soil water potential maximally rose to −15, −35, or −55 kPa for WW, MD, and SD, respectively, after re-watering. Total water application during the whole growing season and the dynamic changes of soil water potential were shown in Supplementary Fig. The experiment was a two-by-three factorial design . Each treatment comprised 48 pots as replicates in a completely randomized block design. From 7 tillers in the main stem to maturity (1-digit code: 9)59 (42 DAA) for both cultivars since they had very similar growth periods. The sampled spikes were divided into three groups (5 spikes each) as subsamples for the measurement of SSS and GBSS activities, and grain weight. All grains from each spikelet were removed. The sampled grains were frozen in liquid nitrogen for 2 min and then stored at −80 °C for enzymatic measurements. The grain filling process was fitted by Richards’s Growth Equation (Richards 1959) according to the method described by Zhu et al.60.G) was calculated as the derivative of equation ; and B, k, and N are the regression coefficients. The period of active grain filling is defined as the time interval taken for W to change from 5% (t1) to 95% (t2) of A. The average rate of grain filling during this period was calculated from t1 to t2.A total of 200 spikes that headed on the same day were chosen and tagged for each treatment. Fifteen tagged spikes from each treatment were sampled at 6-day intervals from anthesis (1-digit code: 6) to maturity (1-digit code: 9)Plants in 10 pots of each treatment were harvested at maturity (1-digit code: 9) for the determination of grain yield. Yield components, i.e., number of spikes, grains per spike, and 1000-grain weight were determined from 50 plants in each treatment.Measurements of leaf water potentials of the upmost fully-expanded leaves on stems were made at predawn (06:00 h) and midday (11:30 h) at 0, 12, 24, 36, 48, 60, 72 days after soil moisture treatment, respectively. Three pressure chambers  were used for leaf water potential measurement, with six leaves for each treatment.et al.61 the sampled grains (180-220 mg) were homogenized in a pre-cooled mortar containing 1 mL of extraction buffer comprising, 100 mM HEPES-NaOH (pH 7.6), 5 mM MgCl2, 5 mM dithiothreitol (DTT), 2 mM EDTA, 12.5% (v/v) glycerol, and 5% (w/v) insoluble polyvinylpyrrolidone 40. The homogenate was centrifuged at 12000 × g for 10 min, and then the supernatant was collected for the SSS activity assay. The supernatant was re-suspended in 1 mL of extraction buffer for GBSS activity analysis. The SSS and GBSS activities in the grains were determined by the method of Wang et al.23. All chemicals and enzymes used for enzymatic measurement were from Sigma Chemical Company . All the enzyme activities were expressed as nmol mg−1 protein min−1.Three replications were performed for each treatment and for all measurements below. The method for soluble starch synthase (SSS) and granule bound starch synthase (GBSS) extract was described by Zhu et al.62 after a minimum storage period, and was defined as the sum of amylose and amylopectin. Briefly, the sampled grains were ground in mortar, and then the powder was degreased twice with anhydrous ether. A 100 mg fraction of each sample was used to determine amylose and amylopectin contents. A calibration curve was derived using pure amylose and amylopectin from potato and maize, respectively.Total starch accumulation in the wheat grains was determined using the method described by Zhu et al.53 with some modifications. Briefly, wheat flour of 10 g from each treatment was steeped in NaOH solution (pH = 9.5) with 50 mg·g−1 alkaline protease at 42 °C for 24 h to remove protein. The starch slurry was through eight layers of cotton gauze and 200-mesh sieves, and the filtrate was collected. The filtrate was centrifuged at 3,000 × g for 10 min, and then the supernatant was discarded. The faintly colored supernatant liquid was carefully scraped off, while the remaining white precipitate was re-suspended with 20 mL of deionized water, centrifuged at 3,000 × g for 10 min, and the supernatant was again removed. The aforementioned centrifugal steps were repeated five times to ensure thorough removal of impurities. Finally, the starch was dried at 30 °C at ambient pressure and the dried starch was put through a 200-mesh sieve, and then stored in a closed dry container until further analysis.Starch was isolated from the peeled grains according to the method of Gao The particle size characteristics of the starch were determined using an MS-2000 laser particle size analyser . The instrument uses laser light scattering to measure sizes between 0.2 and 2000 μm. The dispersed phase was absolute ethyl alcohol. Starch granule size distributions were measured using the native instrument software and expressed as percentages.et al.63. Briefly, starch was defatted in methanol/water  at 65 °C for 1 h and dissolved in urea dimethyl sulfoxide (UDMSO) solution at 95 °C for 1 h. The starch-UDMSO solution was treated with I2-KI solution. The apparent amylase content was calculated from the absorbance at 620 nm by reference to a calibration curve which was derived using pure amylose and amylopectin from potato and maize, respectively.The apparent amylose content in starch was determined using the iodometric method described by Man et al.64 and Tran et al.65. The molecular weight distribution of the debranched starch was determined by gel-permeation chromatography (GPC) using a PL-GPC 220 system , which included three columns  and a differential refractive index detector, according to the method of Cai et al.66. The dimethyl sulphoxide (DMSO) containing 0.5 mM NaNO3 was used as eluent system at a flow rate of 0.8 mL min−1. The column oven temperature was controlled at 80 °C. Standard dextrans  were used for column calibration and, on the basis of the standards, and the relative molecular weight (molecular size) was calculated.Starch was deproteinised with protease and sodium bisulfite, and then debranched according to the methods of Li et al.67, using a high-performance anion-exchange chromatograph  equipped with a pulsed amperometric detector, guard column, CarboPacTM PA200 analytical column, and AS-DV autosampler. Briefly, the starch was deproteinized with protease and sodium bisulfite, and then debranched with isoamylase. Debranching was terminated by adding NaOH and heating to 80 °C. The sample was centrifuged at 10,000 × g for 10 min, filtered (0.22-μm nylon filter) and injected into an HPAEC system with a pulse amperometric detector (PAD) system. Eluent A was 150 μM NaOH, and eluent B was 150 μM NaOH with 500 μM sodium acetate. The gradient of eluent B was 35% from 0 to 2 min, increased from 35% to 60% for 15 min and from 60% to 80% for 13 min, maintained 80% for 10 min, and finally reduced from 80% to 30% for 0.2 min. The separations were carried out at 25 °C with a flow rate of 0.5 mL min−1. Maltohexaose was used as a standard. The chain length distribution was characterized as a percentage of the total peak area.The chain length distributions in the debranched samples were analysed using the method of Lin et al.68. Before measurements, all specimens were stored in a desiccator for one week at a constant humidity (relative humidity = 75%) maintained by saturated brine.Starch XRD patterns were obtained using a D8 Advance diffractometer . The diffractometer was operated at 200 mA and 40 kV. The scanning region of the diffraction angle (2θ) ranged from 3° to 40° at a step size of 0.02° and counting time of 0.8 s. XRD analysis and determination of the relative crystallinity (%) of starch were carried out following the method described by Wei −1 using a differential scanning calorimeter .Starch (5.0 mg) was precisely weighed and mixed with distilled water (15 μL). The mixture was sealed in an aluminium pan overnight at 4 °C. After equilibrating for 1 h at room temperature, the starch sample was then heated from 25 to 130 °C at a rate of 10 °C min2O (1 mL). The samples were heated to a constant temperature of 90 °C in a shaking water bath for 1 h and then centrifuged at 3,000 × g for 15 min. The supernatant was removed, and the starch deposit was weighed in the centrifuge tube. The residue was dried to a constant weight at 70 °C for 48 h, and reweighed. Swelling power (g g−1) was calculated using the following equation:1 is the weight of the centrifuge tube, m2 is the total weight of the centrifuge tube and undried residue, and m3 is the total weight of the centrifuge tube and dried residue.Starch (40 mg) was placed into a centrifuge tube with double-distilled HAspergillus niger amyloglucosidase  following the method described by Huang et al.69. The hydrolysis time points were 1, 2, 4, 8, 12, 24, 48, and 72 h for PPA and AAG, and 0.5, 1, 2, 4, 6, 8, 10, and 14 days for HCl. After hydrolysis, the starch slurry was quickly centrifuged  at 4 °C for 5 min. The supernatant was used to measure soluble carbohydrates using the anthrone-H2SO4 method to quantify the degree of hydrolysis.Starch was hydrolysed using hydrochloric acid (HCl), porcine pancreatic α-amylase , or In vitro starch digestion was analysed using the method described by Huang et al.69. Native starch (10 mg) was mixed with distilled water (2 mL) in a centrifuge tube and heated at 98 °C for 12 min to prepare gelatinised starch. The gelatinised starch was stored at 4 °C for 36 h to prepare retrograded starch. The starch (10 mg) was then incubated in enzyme solution , 4 U AAG (Megazyme E-AMGDF)) and digestion was conducted in an Eppendorf Thermo Mixer at 37 °C with continuous shaking  for 20 and 120 min. Enzyme treatment was terminated by adding 0.1 M HCl (240 mL) and 50% ethanol (2 mL) and centrifuging . The glucose content in the supernatant was determined using the D-Glucose (GOPOD Format) assay kit . Starch nutritional fractions, based on the rate of hydrolysis, were denoted as rapidly digestible starch , slowly digestible starch , and resistant starch .P0.05 level (LSD0.05).Statistical analyses of the results for variance were carried out using the SAS/STAT statistical analysis package . The statistical model used included sources of variation due to replication, year, variety, soil moisture treatment, and the interaction of year × variety, year × treatment and cultivar × treatment. Data from each sampling date were analysed separately, and means were tested using least significant differences at the Supplementary Information