Information

Do flies avoid infrared light?

Do flies avoid infrared light?


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

If flies avoid IR light, many places can be kept free from flies using IR light. We see insect repellers in many restaurants, but they don't seem to work well. Can IR be a safe & effective alternative?


In the last decade, it has been proposed that the sun's IR-A wavelengths might be deleterious to human skin and that sunscreens, in addition to their desired effect to protect against UV-B and UV-A, should also protect against IR-A (and perhaps even visible light). Several studies showed that NIR may damage skin collagen content via an increase in MMP-1 activity in the same manner as is known for UVR. Unfortunately, the artificial NIR light sources used in such studies were not representative of the solar irradiance.

Yet, little has been said about the other side of the coin. This article will focus on key information suggesting that IR-A may be more beneficial than deleterious when the skin is exposed to the appropriate irradiance/dose of IR-A radiation similar to daily sun exposure received by people in real life.

IR-A might even precondition the skin – a process called photoprevention – from an evolutionary standpoint since exposure to early morning IR-A wavelengths in sunlight may ready the skin for the coming mid-day deleterious UVR.

Consequently IR-A appears to be the solution, not the problem. It does more good than bad for the skin. It is essentially a question of intensity and how we can learn from the sun.


Bank Hard! Flies Fly Like Fighter Jets to Evade Predators

Catching a fly isn't easy, as anyone who's ever tried to swat one knows. Why are they so hard to catch? It could be because they maneuver like fighter jets, a new study shows.

Using high-speed video cameras, a team of researchers captured the lightning-fast wing and body motion of fruit flies as the insects performed rapid, banked turns to avoid a looming threat. The team also used giant, robotic flies to understand how the sprightly pests performed these aerobatics.

The species of fruit flies in the study, Drosophila hydei, is known for its excellent flying ability. Scientists have sometimes compared their flight to "swimming" through the air, but the flies' airborne gymnastics are closer to those of a fighter pilot, the researchers said. [See Video of Flies Flying Like Fighter Jets]

"These flies roll up to 90 degrees &mdash some are almost upside down &mdash to maximize their force, and escape," said study researcher Florian Muijres, who studies the biomechanics of flight and swimming at the University of Washington in Seattle.

The flies can change course in less than 1/100th of a second &mdash 50 times faster than the blink of an eye, the researchers said.

It's a bird, it's a plane…

Muijres and his team took fruit flies, which are about the size of a sesame seed, and put them in an arena where they were free to buzz around. High-speed cameras filming at 7,500 frames per second were focused on the middle of the arena, where two lasers were set up. The cameras require very bright light to function, but regular light would have blinded the flies. So, instead, the team flooded the arena with infrared light (which is invisible to flies and humans).

When a fly flew through the lasers, it triggered an expanding black shadow to appear, resembling a predator or an obstacle, which prompted the fly to take evasive action.

The flies performed amazing feats of agility. As the looming shadow approached, the flies tipped their bodies in screeching midair turns. The insects usually flap their wings 200 times per second, but to avoid the shadow, they were able to change direction with barely more than a single flap, producing a force that propelled them away from the danger.

To execute these movements, the fly's brain must be performing a sophisticated calculation, Muijres said. "The fact that flies roll to the side is maybe not that surprising," he said, but "the surprise is really the accuracy and speed combination."

Fruit flies have tiny brains, yet they are capable of flight maneuvers that are much more complex than those of many other flying insects. For example, moths have larger brains than flies do, but they dive straight into the ground to avoid being caught by bats.

What's more, the fly's behavior "is very fast, and it's completely innate &mdash the fly doesn't need to learn how to do this," said senior study co-author Michael Dickinson, a biologist at the University of Washington. Exactly how the fly's brain executes these stunts is something the researchers plan to investigate in future studies.

To model the flies' flight dynamics, the researchers put a giant robotic fly, with a 2-foot (0.6 meters) wingspan, in a vat of mineral oil. Scientists have used such robot flies, which have names like "RoboFly" of "Bride of RoboFly," for years to study these insects' flight on a much larger scale.

Given this newfound knowledge of fly flight, what's best way to catch a fly?


Scientists discover why flies are so hard to swat

(PhysOrg.com) -- Over the past two decades, Michael Dickinson has been interviewed by reporters hundreds of times about his research on the biomechanics of insect flight. One question from the press has always dogged him: Why are flies so hard to swat?

"Now I can finally answer," says Dickinson, the Esther M. and Abe M. Zarem Professor of Bioengineering at the California Institute of Technology (Caltech).

Using high-resolution, high-speed digital imaging of fruit flies (Drosophila melanogaster) faced with a looming swatter, Dickinson and graduate student Gwyneth Card have determined the secret to a fly's evasive maneuvering. Long before the fly leaps, its tiny brain calculates the location of the impending threat, comes up with an escape plan, and places its legs in an optimal position to hop out of the way in the opposite direction. All of this action takes place within about 100 milliseconds after the fly first spots the swatter.

"This illustrates how rapidly the fly's brain can process sensory information into an appropriate motor response," Dickinson says.

For example, the videos showed that if the descending swatter--actually, a 14-centimeter-diameter black disk, dropping at a 50-degree angle toward a fly standing at the center of a small platform--comes from in front of the fly, the fly moves its middle legs forward and leans back, then raises and extends its legs to push off backward. When the threat comes from the back, however, the fly (which has a nearly 360-degree field of view and can see behind itself) moves its middle legs a tiny bit backwards. With a threat from the side, the fly keeps its middle legs stationary, but leans its whole body in the opposite direction before it jumps.

"We also found that when the fly makes planning movements prior to take-off, it takes into account its body position at the time it first sees the threat," Dickinson says. "When it first notices an approaching threat, a fly's body might be in any sort of posture depending on what it was doing at the time, like grooming, feeding, walking, or courting. Our experiments showed that the fly somehow 'knows' whether it needs to make large or small postural changes to reach the correct preflight posture. This means that the fly must integrate visual information from its eyes, which tell it where the threat is approaching from, with mechanosensory information from its legs, which tells it how to move to reach the proper preflight pose."

The results offer new insight into the fly nervous system, and suggest that within the fly brain there is a map in which the position of the looming threat "is transformed into an appropriate pattern of leg and body motion prior to take off," Dickinson says. "This is a rather sophisticated sensory-to-motor transformation and the search is on to find the place in the brain where this happens," he says.

Dickinson's research also suggests an optimal method for actually swatting a fly. "It is best not to swat at the fly's starting position, but rather to aim a bit forward of that to anticipate where the fly is going to jump when it first sees your swatter," he says.

The paper, "Visually Mediated Motor Planning in the Escape Response of Drosophila," will be published August 28 in the journal Current Biology.


Why are flies attracted to light?

You don’t need to be a professional pest controller to know that insects such as flies are attracted to light. Think of all those times you’ve seen moths, beetles and other insects frantically flying around light fixtures and street lamps when it’s dark.

Yet a greater understanding of this natural attraction to light could help to develop more effective fly killers (insect light traps) and improve fly control solutions. Research into this topic is of vital importance to businesses in the food industry, like food processing and food retail even hospitality, helping them to avoid outbreaks of fly-borne diseases such as Salmonellosis, Dysentry and Gastroenteritis.

Rentokil’s Global Technical Centre has scientists investigating the physics of how light impacts the biological attraction of flies to a trap. This research has helped to uncover LED technology as a much more effective insect attractant than other conventional light sources.

When LED technology is combined with an effective fly killer unit, it offers the opportunity to capture and eliminate more flies than other conventional fly traps.

In fact, this research and the expertise of the technical team has helped Rentokil develop a new Lumnia fly control unit using LED technology to attract, eliminate and encapsulate flies effectively and hygienically.

What is it about the light from LEDs that is so attractive to flies?

The way light is emitted from LEDs is the reason they are particularly attractive to certain insects. LEDs produce UV-A as intense beams of light, which penetrate further into the surrounding space than light phosphor lamps, for example. House flies are particularly attracted to UV-A as their eyes are sensitive to light at that wavelength.

There is no single scientific explanation as to why flies are attracted to light. There are several theories which offer a possible explanation, as outlined below:

Using light for safety

For some insects, a bright light source may be seen as an emergency beacon. When in doubt, these insects instinctively head towards light sources, which are generally positioned on higher ground than the hazardous environment they are currently in. Light can for some insects, acts as a familiar safety signal, just as air bubbles leading the way to the water surface might help for other creatures.

Using light for navigation

Another popular theory for attraction to light, is that insects use it as a navigational aid. An insect flying north for example, is able to judge its direction by keeping a natural source of light, such as the sun or moon, on its right. This method works well as long as the source of light remains both constant and at a distance.

If an insect encounters a round incandescent porch light, however, it becomes confused by its source. This explains the peculiar behaviour of a moth continuously encircling a light source – it instinctively wants to keep the light on a certain side of its body whilst navigating its route.

Phototaxis – an attraction to light

The difference between insects that are attracted to light and those which are not is a phenomenon known as phototaxis. Certain insects, such as cockroaches or earthworms, have negative phototaxis, meaning they are repelled by an exposure to light. Moths, flies and many other flying insects have positive phototaxis and are naturally attracted to it.

Debating science

There is some debate in the scientific community over why a positively phototactic insect, like a fly, will continue to hover around an artificial light source even when natural light becomes available. Some believe that the insect is not attracted to the light itself, but the darkness surrounding it.

Others suggest the insect’s eyes, which often contain multiple lenses, struggle to adjust from light to dark, leaving the insect vulnerable to predators whilst night-blind. In this case, the insect may find it safer to remain in the light rather than fly away and become too blind to react to threats and obstacles.

Fly killers and LED technology

Rentokil’s researches are able to prove the efficacy of the Lumnia LED fly control unit through a standard Half-Life measure test. The Half-Life measure represents the time taken to eliminate 50% of flies released in a test chamber. The lower the Half-Life measure, the more effective the fly control unit.

An effective fly control solution must also consider the correct placement of a fly unit, given what we know about phototaxis.

The placement of fly control units with respect to local light sources is of critical importance to their effectiveness.

This in-depth understanding of how light impacts the biological attraction of flies and other insects to a fly control unit demonstrates the complexity of pest control issues and the expertise, knowledge and experience required to successful control flies in a business premises.


Discussion

Anyone seeking to unravel the neural logic underlying a navigation process—whether it is the response of a cell to a morphogen gradient or the flight of a seabird guided by the Earth’s magnetic field—faces the need to characterize the basic orientation strategy before speculating about its molecular and cellular underpinnings. PiVR is a versatile closed-loop experimental platform devised to create light-based virtual sensory realities by tracking the motion of unconstrained small animals subjected to a predefined model of the sensory environment (Fig 1). It was created to assist the study of orientation behavior and neural circuit functions by scholars who might not have extensive background in programming or instrumentation to customize existing tools.

An experimental platform that is inexpensive and customizable

Prior to the commercialization of consumer-oriented 3D printers and cheap microcomputers such as the Raspberry Pi, virtual reality paradigms necessitated using custom setups that cost several thousand or even one hundred thousand dollars [11,13,18,19]. PiVR is an affordable (< US$500) alternative that enables laboratories without advanced technical expertise to conduct high-throughput virtual reality experiments. The procedure to build PiVR is visually illustrated in S7 Movie. All construction steps are intended to be tractable by virtually any users who have access to a soldering station and a 3D printer. A detailed step-by-step protocol is available on a dedicated website (www.pivr.org).

The creation of realistic immersive virtual realities critically depends on the update frequency and the update latency of the closed-loop system. The maximal update frequency corresponds to the maximally sustained frame rate that the system can support. The update latency is the latency between an action of the tested subject and the implementation of a change in the virtual reality environment. In normal working conditions, PiVR can be used with an update frequency of up to 70 Hz with a latency below 30 milliseconds (S2 Fig). These characteristics are similar to those routinely used to test optomotor responses with visual display streaming during walking behavior in insects [21], thereby ensuring the suitability of PiVR for a wide range of applications.

Different optogenetic tools require excitation at different wavelengths ranging from blue to deep red [26,51,52]. The modularity of PiVR enables the experimenter to customize the illumination system to any wavelength range. Additionally, different animals demand distinct levels of light intensities to ensure adequate light penetration in transparent and opaque tissues. Although 5 μW/mm 2 of red light had been used to activate neurons located in the leg segments of adult flies with CsChrimson [38], 1 μW/mm 2 is enough to activate OSNs of the semitransparent Drosophila larva (Fig 2). In its standard version, PiVR can emit red-light intensities as high as 2 μW/mm 2 and white-light intensities up to 6,800 Lux—a range sufficient for most applications in transparent animals (Figs 2 and 4). For animals with an opaque cuticle, we devised a higher-power version of the backlight illumination system that delivers intensities up to 22 μW/mm 2 (525 nm) and 50 μW/mm 2 (625 nm) (Fig 3 and S1D Fig). Given that an illumination of 50 μW/mm 2 is approaching the LED eye safety limits (International Electrotechnical Commission: 62471), it is unlikely that experimenters will want to exceed this range for common applications in the lab.

Exploring orientation behavior through virtual reality paradigms

By capitalizing on the latest development of molecular genetics and bioengineering [1], virtual sensory stimuli can be created by expressing optogenetic tools in targeted neurons of the peripheral nervous system of an animal. In the present study, PiVR was used to immerse Drosophila in virtual chemosensory gradients (Figs 2 and 3). In a first application, we stimulated one OSN of Drosophila larvae with CsChrimson. The attractive search behavior elicited by a single source of a real odor (Fig 2Di) was reproduced in an exponential light gradient (Fig 2Ei). To reveal the precision with which larvae orient their turns toward the gradient, larvae were tested in a virtual-odor landscape with a volcano shape (Fig 2Fi).

Adult flies move approximately 10 times faster than larvae. We established the ability of PiVR to immerse freely moving flies in a virtual bitter-taste gradient (Fig 3B). Unlike the attractive responses elicited by appetitive virtual odors in the larva (positive chemotaxis), bitter taste produced strong aversive behavior (S5 Movie). A correlative analysis of data recorded with PiVR revealed that the aversive behavior of adult flies is at least partly conditioned by a modulation of the animal’s locomotor speed: random search is enhanced upon detection of (virtual) bitter, whereas locomotion is drastically reduced upon sensory relief. As illustrated in Fig 3, PiVR offers a framework to explore the existence of other mechanisms contributing to the orientation of flies experiencing taste gradients. This approach adds to earlier studies of the effects of optogenetic stimulation of the bitter taste system on spatial navigation [38] and feeding behavior [53].

Zebrafish move through discrete swim bouts. The loop time of PiVR was sufficiently short to rise to the tracking challenge posed by the discrete nature of fish locomotion (Fig 4B). Genuine phototactic behavior was elicited in zebrafish larvae for several minutes based on pure temporal changes in light intensity devoid of binocular differences and a panoramic component. By synthetically recreating naturalistic landscapes [45], the behavior recorded by PiVR in Gaussian light gradients (Fig 4) complements previous studies featuring the use of discrete light disks [46] and local asymmetric stimulations [54]. A correlative analysis of the sensory input and the behavioral output corroborated the idea that positive phototaxis in fish can emerge from a modulation of the turn rate by the detected changes in light intensity (Fig 4G and 4H) without necessarily involving a turning bias toward the light gradient (Fig 4J). This orientation strategy shares similarities with the nondirectional increase in locomotor activity (“dark photokinesis”) that follows a sudden loss of illumination [55,56]. Taken together, our results establish that PiVR is suitable to conduct a detailed analysis of the sensorimotor rules directing attractive and aversive orientation behavior in small animals with distinct body plans and locomotor properties.

Outlook

Although this study focused on sensory navigation, PiVR is equally suited to investigate neural circuits [4] through optogenetic manipulations. To determine the connectivity and function of circuit elements, one typically performs acute functional manipulations during behavior. PiVR permits the time-dependent or behavior-dependent presentation of light stimuli to produce controlled gain of functions. The use of multiple setups in parallel is ideal to increase the throughput of behavioral screens—a budget of US$2,000 is sufficient to build more than five setups. If the experimenter wishes to define custom stimulation rules—triggering a light flash whenever an animal stops moving, for instance—this rule can be readily implemented by PiVR on single animals. For patterns of light stimulation that do not depend on the behavior of an animal—stimulations with a regular series of brief light pulses, for instance—groups of animals can be recorded at the same time. In its standard configuration (Fig 1A), the resolution of videos recorded with PiVR is sufficient to achieve individual tracking of group behavior through off-line analysis with specialized algorithms such as idtracker.ai [25] (S2Bi Fig). Even for small animals such as flies, videos of surprisingly good quality can be recorded by outfitting the charge-coupled device (CCD) camera of PiVR with appropriate optics (S8 Movie).

Until recently, systems neuroscientists had to design and build their own setup to examine the function of neural circuits, or they had to adapt existing systems that were often expensive and complex. Fortunately, our field has benefited from the publication of a series of customizable tools to design and conduct behavioral analysis. The characteristics of the most representative tools are reviewed in S2 Table. The ethoscope is a cost-efficient solution based on the use of a Raspberry Pi computer [23], but it was not designed to create virtual realities. Several other packages can be used to track animals in real time on an external computer platform. For instance, Bonsai is an open-source visual programming framework for the acquisition and online processing of data streams to facilitate the prototyping of integrated behavioral experiments [57]. FreemoVR can produce impressive tridimensional virtual visual realities [11]. Stytra is a powerful open-source software package designed to carry out behavioral experiments specifically in zebrafish with real-time tracking capabilities [22]. As illustrated in comparisons of S2 Table, PiVR complements these tools by proposing a versatile solution to carry out closed-loop tracking with low-latency performance. One limitation of PiVR is that it produces purely homogenous temporal changes in light intensity without any spatial component. Because of its low production costs, the simplicity of its hardware design, and detailed documentation, PiVR can be readily assembled by any laboratory or group of high school students having access to a 3D printer. For these reasons, PiVR represents a tool of choice to make light-based virtual reality experiments accessible to experimentalists who might not be technically inclined.

We anticipate that the performance (S1 Fig) and the resolution of PiVR (Methods) will keep improving in the future. Historically, a new and faster version of the Raspberry Pi computer has been released every 2 years. In the near future, the image processing time of PiVR might decrease to just a few milliseconds, pushing the frequency to well above 70 Hz. Following the parallel development of transgenic techniques in nontraditional genetic model systems, it should be possible to capitalize on the use of optogenetic tools in virtually any species. Although PiVR was developed for animals measuring no more than a few centimeters, it should be easily scalable to accommodate experiments with larger animals such as mice and rats. Together with FlyPi [58] and the ethoscope [23], PiVR represents a low-barrier technology that should empower many labs to characterize new behavioral phenotypes and study neural circuit functions with minimal investment in time and research funds.


MATERIALS AND METHODS

Flies

All experiments were performed on 3-day-old mated female fruit flies, Drosophila melanogaster Meigen, selected from a laboratory population descended from 200 wild-caught females. The flies were maintained at 25°C and ambient humidity (20–40%) on a 16 h:8 h light:dark cycle. One day before each experimental trial was performed, we anesthetized the flies on a cold plate held at 4°C. The wings of the flies were clipped between the first and second cross-veins, approximately half the length of the wing. If a fly's gravitational sense was to be impaired, it was also done at this time, by immobilizing the joint between the second and third antennal segments with a UV-cured glue (Budick et al., 2007). The flies were allowed to recover with food overnight and then deprived of food, but not water, 10–14 h before the experiments were performed. All experiments were performed during the evening peak in their circadian activity cycle (Shafer et al., 2004). The flies were placed into individual vials with a water source and allowed to acclimate to experimental light levels for at least 30 min prior to experiments. Each fly was used only once, and all trials consisted of a single fly recorded for 10 min.

Walking arena

In order to study the behavior of flies exploring a topologically complex environment we developed a large, free-walking arena. The arena consisted of a 24.5 cm diameter black disk surrounded by a 24.5 cm tall backlit cylindrical panorama of randomized black squares with a 50% filling probability which provided a background visual stimulus (Fig. 1A). As viewed from the center of the arena each square subtended 5 deg. The paper printed with the panorama was backlit by a circular array of eight 35 W halogen lights (Fig. 1B). Flies were maintained within the arena using a thermal barrier, which proved easier to regulate and much more effective than either a water moat or a wall coated with Fluon™ (A.A.R. and M.H.D., unpublished data). Most flies approached the thermal barrier and turned away rare experiments in which flies did escape over the barrier before the end of the 10 min trial were discarded. Two versions of the arena were used in these experiments simply due to methodological improvements that were made during the course of the study. Arena 1 was equipped with a water heated thermal barrier and a passive cooling system (Fig. 1C) whereas Arena 2 was equipped with an electrically heated thermal barrier and an active cooling system (Fig. 1D). Although both systems worked, the active electrical system is easier to fabricate and permits more precise control of surface temperature. All trials were performed in Arena 2 unless noted otherwise. In all cases in which identical treatments were performed in Arenas 1 and 2, we verified the data were indistinguishable and the results were pooled in subsequent analysis.

In Arena 1 (Fig. 1C), the thermal barrier was 0.64 cm high around the platform. It consisted of a cylindrical aluminum walled chamber heated by 55°C recirculating water. The painted aluminum surface facing the arena was

38°C. An array of four CPU (computer processing unit) fans blowing room air onto the bottom of the acrylic arena floor passively maintained the floor temperature. The surface temperature profile of the arena floor was 24°C at the center and rose gradually to 26°C at a distance of 2 cm from the thermal barrier, beyond which the temperature rose rapidly to 30°C as measured by a thermocouple.

Experimental apparatus. (A) Top-down view of the arena with backlit panorama. The thermal barrier is depicted in red. (B) A schematic side view of the fly visualization setup. Near-IR LEDs (light-emitting diodes) mounted with the camera above the arena, and two of the eight halogen lights arranged in a circular array are depicted. (C) A schematic vertical cross section of Arena 1 with passive cooling. Recirculating hot water heats the thermal barrier and four CPU fans cool the walking platform (only one is depicted). (D) A schematic vertical cross section of Arena 2 with active cooling. The thermal barrier is a strip of galvanized steel wrapped in a rope heater and insulated from the walking platform by a layer of neoprene. The walking platform in actively cooled by a PID-controlled array of four thermoelectric modules with water-cooled heat sinks (only one is depicted). (E) The two arrangements of cones in the arena. The arena floor is shown in grey for illustration purposes only the floor and cones were both painted matte black. (F) The color-code convention used for the cones of equal lateral surface area. The angle between the base and lateral surface, and the height, are noted below each cone.

Experimental apparatus. (A) Top-down view of the arena with backlit panorama. The thermal barrier is depicted in red. (B) A schematic side view of the fly visualization setup. Near-IR LEDs (light-emitting diodes) mounted with the camera above the arena, and two of the eight halogen lights arranged in a circular array are depicted. (C) A schematic vertical cross section of Arena 1 with passive cooling. Recirculating hot water heats the thermal barrier and four CPU fans cool the walking platform (only one is depicted). (D) A schematic vertical cross section of Arena 2 with active cooling. The thermal barrier is a strip of galvanized steel wrapped in a rope heater and insulated from the walking platform by a layer of neoprene. The walking platform in actively cooled by a PID-controlled array of four thermoelectric modules with water-cooled heat sinks (only one is depicted). (E) The two arrangements of cones in the arena. The arena floor is shown in grey for illustration purposes only the floor and cones were both painted matte black. (F) The color-code convention used for the cones of equal lateral surface area. The angle between the base and lateral surface, and the height, are noted below each cone.

In Arena 2 (Fig. 1D), the thermal barrier was flush with the top surface of the arena floor. It consisted of a 0.2 mm thick, 24 mm wide band of galvanized steel wrapped with a thin electric rope heater (OmegaLux, Stamford, CT, USA), powered by a variable AC transformer (Staco, Dayton, OH, USA) in open loop. The arena floor was insulated from the thermal barrier by a thin strip of neoprene. The arena floor was constructed of a 0.6 cm thick aluminum plate with four circular thermoelectric (TE) modules (TE Technology, Inc., Traverse City, MI, USA) bolted to the underside, each with a water-cooled temperature exchanger. A thermistor, mounted at the center of the underside of the floor, provided input to a proportional integral derivative (PID) controller driving the four TE modules in parallel with a set point of 25°C. The surface temperature of the arena floor varied by less then 1°C as measured by a non-contact infrared thermometer (OmegaScope, Stamford, CT, USA).

Flies were introduced into the arena by placing them into a black vial with a neck that fitted securely into a 3 mm hole in the arena floor. Each fly was allowed to crawl up the vial and out onto the surface of the arena, thereby avoiding the effects of mechanical agitation caused by aspirating flies with a mouth pipette. After the fly entered the arena, the hole was plugged with a stopper that was flush with the arena floor. Flies that did not enter the arena within 1 min were discarded. Of the 191 individual trials attempted for this study with this loading method, only 11 flies (6%) failed to enter the arena by crawling up and out of the black vials. Thus, there is no evidence that our data are biased by inadvertently selecting against flies with weak gravitaxis behavior. In trials using flies that had their antennae manipulated (which do exhibit reduced negative-gravitactic response) we gently tapped the animals into the arena from above. The floor of the arena was washed with detergent and rinsed between each trial.

Fly visualization and tracking

Data were collected using a digital camera mounted 48 cm above the arena floor with a 720 nm high pass optical filter (R72, Hoya Huntington Beach, CA, USA Fig. 1B). The flies were visualized using near-IR (infrared) light, which reflects well off of the fly's cuticle, and the arena floor was painted matte black to maximize contrast. In Arena 1, we used a camera (Scorpion, Point Grey, Richmond, BC, Canada) with 1600×1200 pixel resolution. Image stacks were collected at 10 frames s −1 and analyzed in real time by a custom software program developed in MATLAB (Mathworks, Waltham, MA, USA). In Arena 2, we used a camera with 1280×1024 pixel resolution (A622F, Basler, Exton, PA, USA). Using this camera, images were collected at 20 frames s −1 and analyzed in real time using Motmot, open source camera software written in Python, using the FlyTrax plug-in (Straw and Dickinson, 2009). Both tracking programs determined the fly's two-dimensional (2-D) position and body orientation with 180 deg ambiguity based on background subtraction. The images of the flies in our movies are approximately ten pixels long and five pixels wide. For each frame, cropped images of a 100×100 pixel region around the fly (used for testing automated algorithms) were saved along with the 2-D coordinates of the fly, body axis angle and a time stamp. A single full resolution image of the arena was also saved. All data were collected in Arena 2 unless otherwise noted.

Empty arena

To examine the role of visual input on basic locomotor activity, 66 individual flies were tracked within an empty arena (i.e. void of the conical objects), surrounded by the random checkerboard panorama. Half the flies were tested under lit conditions (450 lux measured at the center of the arena) and half tested in complete darkness. To achieve these conditions, we replaced the translucent cylinder with an opaque black cylinder and all ambient light was eliminated from the room (measured illuminance ≤1 lux). Example trajectories and speed profiles are shown in Fig. 2A,B. We present examples that are representative of the data and have an arena crossing in the fifth minute in order to show the difference in the speed profiles of flies in light versus dark conditions.

Arena with objects

To test the effect of a more complex topology on the flies' exploratory behavior, we placed four right angle cones of equal lateral surface area but of differing heights and slopes in the arena. The geometric dimensions of these cones and the color code that will be used throughout the paper to identify cone type are shown in Fig. 1F. Under these conditions, we performed 45 trials (20 in Arena 1 and 25 in Arena 2). Each object (painted black to match the floor and allow visualization of the flies while they were on the object) was placed in one of four fixed locations, making a square within the arena, but the relative order was randomized between trials (Fig. 1E). The objects were washed with detergent and rinsed between trials. To test whether the assessment of the objects by the flies was absolute or relative, in one set of experiments we removed the tallest, steepest object and arranged the cones in the same grid leaving one spot empty (Fig. 1E) in 24 trials. To test the role of visual input on object exploration we performed another 45 trials in complete darkness (20 in Arena 1 arena and 25 in Arena 2). Example trajectories and speed profiles are shown in Fig. 2C,D. To test the role of gravitational sensation on object exploration we performed 40 trials with flies whose antennae were immobilized at the joint between the second and third segments. Finally, to test the combined effect of the sensory manipulations, we performed 40 trials using flies with immobilized antennae in complete darkness.

Example trajectories and corresponding velocity plots. Each 10 min trajectory is plotted in gray with the fifth minute plotted in black. The speed profile for that same period is plotted on the right. Trials run in darkness are shown with a gray background. In trajectories with cones present, the footprint of each cone is indicated according the color scheme in Fig. 1F. Representative traces where chosen for the following cases: (A) empty arena with lights on, (B) empty arena in darkness, (C) four cones with lights on, and (D) four cones in darkness.

Example trajectories and corresponding velocity plots. Each 10 min trajectory is plotted in gray with the fifth minute plotted in black. The speed profile for that same period is plotted on the right. Trials run in darkness are shown with a gray background. In trajectories with cones present, the footprint of each cone is indicated according the color scheme in Fig. 1F. Representative traces where chosen for the following cases: (A) empty arena with lights on, (B) empty arena in darkness, (C) four cones with lights on, and (D) four cones in darkness.

Data analysis

The positional and orientation data were recorded in real time but were post-processed using custom software written in Python (www.python.org) and MATLAB (Mathworks, Waltham, MA, USA). All trials were reviewed by examining the stored video record with the tracking data superimposed. Any trials with gross tracking errors (e.g. fly position was lost) were discarded and not included in the enumeration of trial numbers used for analysis. Of 266 trials recorded for this study, only six were discarded for tracking errors.

For each trial with cones present, the locations of the cones were digitized and used to determine the periods of the trial in which a fly was exploring each cone. Because of the cone steepness and the central position of the camera, flies exploring the far side of a cone could have been incorrectly classified as ‘off cone’ with the use of a simple digitization based on the footprint of the cone. To prevent this, the digitized footprint was expanded such that a fly whose center did not appear to be within the footprint of the cone, but was indeed on the cone was correctly classified as ‘on cone’. The assignment of ‘on’ or ‘off’ cone was manually checked against the saved video for each trial.

In trials without cones present or ‘off’ cone, the 2-D position of the fly was smoothed with a Kalman smoother (Kevin Murphy's Kalman filter MATLAB toolbox) and used to calculate translational speed and total distance traveled. For trials with cones present, we calculated the 3-D position of the fly on the cones using the tracked 2-D positions, a model of the 3-D structure of the arena, and a standard pinhole camera model. The 3-D model of the arena was created from the known geometry of the arena and cones and hand digitization of the cone positions in each trial. The surface of this model was extruded by 1 mm as an approximation for flies' own height above the floor. Through each 2-D fly position on the calibrated image plane of the camera, we projected a ray (from the 3-D location of the pinhole camera model center) and intersected it with the extruded 3-D model of the arena to find the estimated 3-D position of the fly. We calculated the 3-D positions for a second time with a fly height of 2 mm and used the magnitude of the difference between the two z-position data sets as an estimate of the error in the 3-D positions. The 3-D position of the fly was smoothed with a Kalman smoother using the error estimate to assign the uncertainty in the observation data. We evaluated the quality of the 3-D position estimates on the tallest, steepest cone (and the stop–walk assignment described below) by recording simultaneously with a second camera mounted directly over this cone, and found that both the 3-D estimate and stop–walk assignment were accurately determined.

The temporal structure of the flies' locomotor activity can be coarsely modeled as discrete bouts of walking and stopping (Martin, 2004). We manually assigned walks and stops in a subset of data (both ‘on’ and ‘off’ cone) based on the small format images. Using these classifications as ground truth, we defined stops and walks based on velocity (3-D velocity when ‘on’ cone) using a dual threshold: when the velocity was above the high threshold (2.5 mm s −1 ) the fly was classified as walking and when the velocity was below the low threshold (1 mm s −1 ) the fly was classified as stopped. When the velocity was between the two thresholds it maintained its previous classification until the second threshold is crossed. This Schmitt trigger avoids rapid changes in classification caused by a single threshold based definition. We also defined the minimum walk duration to be 0.1 s (two frames at 20 frames s −1 ) to avoid misclassifying as walks the transient center of mass movements associated with grooming. We defined the minimum stop duration to be 0.1 s to avoid incorrectly assigning as stops the brief decrease in translational speed associated with sharp turns and pauses. Using these criteria, we determined the percentage of time each fly spent walking or stopped and the duration of each walk and stop bout, as well as the mean and maximum translational speeds during each walk bout. We set a maximum walking speed threshold of 50 mm s −1 to filter out rare events in which the wing-clipped flies jumped within the arena. ‘On’ cone locomotor activity statistics were only calculated for trials performed in Arena 2, in which we estimated 3-D velocity. Additionally, we used the estimated fly z-positions to determine the height at which each stop was performed when the flies were ‘on’ cone.

The body orientation ambiguity was resolved using a variation of the Viterbi algorithm in which orientation flips and walking rapidly backwards were penalized (Branson et al., 2009), and we then calculated mean angular speed during walking periods. Using a method for estimating position and orientation error based on trajectory segments of constant velocity (Branson et al., 2009), we found the orientation tracking error to be 1.5 degrees for the ‘off’ cone data. As can be seen in supplementary material Movies 1 and 2, the orientation tracking is highly accurate and it is unlikely an expert human could do better.

Statistics

Much of our data were not normally distributed (nor transformable to normal distribution) therefore, throughout the paper we present the distribution of results using box-and-whisker plots in which the central line (colored magenta when on a colored background) indicates the median, the box outlines the interquartile range of the data, and the whiskers encompass the range from minimum to maximum value, excluding any outliers. Outliers (indicated by a small cross) are values that are more than 1.5 times the interquartile range below or above the 25th or 75th percentiles, respectively.

We used various statistical tests in the analysis of our data depending upon the assumptions of the tests met by the data, we always used the most powerful test possible. If the data were independent and normal, we used a heteroscedastic t-test. If the data were independent but any of the sets being compared were not normal, then we used a Mann–Whitney U-test. In some cases our data were not independent because a fly can only be in one location of the arena at a time. If the data were not independent we used a Wilcoxon signed rank test, and finally if the data had a large number of tied scores we used a Kolmogorov–Smirnov test. Neither the Wilcoxon or Kolmogorov–Smirnov tests require that the data be normal. In all cases where data were being compared multiple times we used a Bonferroni correction for multiple comparisons to adjust the P-value appropriately. All statistical analysis was performed using SPSS (SPSS Inc, Chicago, IL, USA).

To report the results of our significant tests we use a letter code where the groups labeled with the same letter are not significantly different. A group can have more than one label which indicates that it is not significantly different from any of the groups also labeled with any of those letters. For experiments with cones present, we compared the results of the experiments within a trial type, comparing effect of cone type in a given trial condition. Throughout the paper we indicate the results of within trial type hypothesis testing with black lowercase letters. For example, the results of comparing the encounter rates in Fig. 5B are indicated with lowercase letters showing that the blue, green and yellow cones are not significantly different, nor are the yellow and orange cones, e.g. the blue and green cones are significantly different than the orange cone. When multiple trial conditions were tested (such as different sensory manipulations) we also compared the results across trial type, comparing effects of trial conditions on the response to each cone type. We denote the results of across trial type hypothesis testing with uppercase letters (colored to highlight which cone type is being compared). We only compare the same cone type across different trial conditions. For example, the results of comparing the percentage of time spent on the blue cone across trials with different sensory manipulations in Fig. 7 are indicated with uppercase blue letters showing that panels A, B and D are significantly different, but panel C is not significantly different from panel A or B.


ELI5: How are there not billions of fruit flies all over the grocery store?

I bring home a couple peppers and an onion, now a couple weeks later I have tons of fruit flies in my house. What does the grocery store do to keep them from infesting?

Fruit flies generally go after rotten fruit, not regular fruit, because then they can get through its peel or rind to lay and hatch eggs on the nutritious sugary part. The rents and tear in rotting fruit allow the insides to gives off gasses and esters that strongly attract them. Then their eggs hatch and they breed very quickly, taking only a few days to mature into flies that buzz around the area.

Supermarkets do their best to remove damaged or rotten fruit as quickly as possible, and they usually have a very high turnover of produce so nothing sits around and goes bad like the apple that fell behind the bowl out of site (Edit: sight sheesh!) or the pepper that was mishandled and cracked open on the wall-facing side of the counter.

Traffic is also very high unlike areas of our house. The minute the produce section guy spots a few fruit flies buzzing around something, they get rid of it to avoid disgusting their customers. The produce department is not left unattended for the hot part of a full day, unlike our kitchens or larders when we're at work.


Recommended Cluster Fly Control Products

Insecticide Concentrates

  • LambdaStar UltraCap 9.7 is a long term residual and does not break down easily outside on wall surfaces.
  • It is odorless and does not leave a stain.
  • LambdaStar UltraCap 9.7 can be used as a perimeter treatment.
  • Other encapsulated products that are long term and low odor are, D-Fense SC and Cyzmic CS
  • Cyper WSP or Demon WP with Cypermethrin are orderless products, but will leave a visible residue seen against dark surfaces.
  • Cyper WSP is not labeled for ground, broadcast spraying.

Insecticide Dusts

You can also use a dust D-Fense Dust. Dust this into all cracks and crevices. The dust will flow into the void areas. Pay attention to the west and south walls and caulk any openings.

Insecticide Aerosols

Use aerosols with pyrethrins such as: CB-80 or PT 565, These are pyrethrum contact aerosols that can be used as space sprays to kill on contact. Spray lightly and repeat spray as needed.

Identifying the Cluster Fly (Pollenia rudis)

The cluster fly averages between 1/4 to 3/8 inch long. They are dark gray, never metallic blue or green. When crushed, they give off an odor like buckwheat honey. Cluster flies closely resemble house flies, but they are usually larger and have a yellowish sheen on the thorax. They move slowly. They gather in cluster or large numbers, particulary around windows.

Biology and Habits of Cluster Flies

The cluster fly is a parasite of earthworms and breeds outdoors in lawns and fields during the spring and summer. You can find cluster flies almost everywhere in the United States and Canada, except for the Southern states bordering the Gulf of Mexico. They do not cause a health concern, because they do not lay their eggs in human food.

Female Cluster Flies lay their eggs in cracks in the soil, which hatch in three days. The larvae use earthworms as a food source. The larvae feed for about 22 days. After that, they go into the pupae stage, which lasts 11-14 days before emerging as adults. Adult flies feed on flowers. There are about four generations hatched per summer.

When fall approaches, the cluster flies begin to enter structures in large numbers. Problems with cluster flies begin in late August as they move to winter quarters to over-winter. The cluster fly is seeking warm sites with protective cracks for shelter, crawling back as far as they can get. It is important to consider treatment before this happens.

Cluster flies have been known to squeeze around the edges of windows that are weather-proofed. As the number of cluster flies attracted to the building increases, large clusters of flies huddle inside wall voids, attics, and false ceilings. Most infestations occur in the upper regions of buildings, such as the attics of homes. In multi-story buildings, the cluster flies can be found in the upper two or three floors, and almost always of the south and west sides of the buildings.

If you have unseasonably warm weather in the late fall or winter, the cluster fly may emerge thinking it is spring, going for the warmer air outside. Cluster flies fly very slowly when they just wake up. They are strongly attracted to light, so they are usually found around windows. At night, they are attracted to lamps.

Cluster Fly Inspection

Check around windows for live or dead flies. If you can find the voids in which they are over-wintering, you can treat the voids with a dust or aerosol, but that is not an easy task. In most cases, the voids can't be located.

To locate the voids, start with an inspection of cracks and crevices on the southern and western exterior walls. Usually the only accessible voids are the attics, crawls paces and false ceilings.


Newsroom

CORVALLIS, Ore. – Prolonged exposure to blue light, such as that which emanates from your phone, computer and household fixtures, could be affecting your longevity, even if it’s not shining in your eyes.

New research at Oregon State University suggests that the blue wavelengths produced by light-emitting diodes damage cells in the brain as well as retinas.

The study, published today in Aging and Mechanisms of Disease, involved a widely used organism, Drosophila melanogaster, the common fruit fly, an important model organism because of the cellular and developmental mechanisms it shares with other animals and humans.

Jaga Giebultowicz, a researcher in the OSU College of Science who studies biological clocks, led a research collaboration that examined how flies responded to daily 12-hour exposures to blue LED light – similar to the prevalent blue wavelength in devices like phones and tablets – and found that the light accelerated aging.

Flies subjected to daily cycles of 12 hours in light and 12 hours in darkness had shorter lives compared to flies kept in total darkness or those kept in light with the blue wavelengths filtered out. The flies exposed to blue light showed damage to their retinal cells and brain neurons and had impaired locomotion – the flies’ ability to climb the walls of their enclosures, a common behavior, was diminished.

Some of the flies in the experiment were mutants that do not develop eyes, and even those eyeless flies displayed brain damage and locomotion impairments, suggesting flies didn’t have to see the light to be harmed by it.

“The fact that the light was accelerating aging in the flies was very surprising to us at first,” said Giebultowicz, a professor of integrative biology. “We’d measured expression of some genes in old flies, and found that stress-response, protective genes were expressed if flies were kept in light. We hypothesized that light was regulating those genes. Then we started asking, what is it in the light that is harmful to them, and we looked at the spectrum of light. It was very clear cut that although light without blue slightly shortened their lifespan, just blue light alone shortened their lifespan very dramatically.”

Natural light, Giebultowicz notes, is crucial for the body’s circadian rhythm – the 24-hour cycle of physiological processes such as brain wave activity, hormone production and cell regeneration that are important factors in feeding and sleeping patterns.

“But there is evidence suggesting that increased exposure to artificial light is a risk factor for sleep and circadian disorders,” she said. “And with the prevalent use of LED lighting and device displays, humans are subjected to increasing amounts of light in the blue spectrum since commonly used LEDs emit a high fraction of blue light. But this technology, LED lighting, even in most developed countries, has not been used long enough to know its effects across the human lifespan.”

Giebultowicz says that the flies, if given a choice, avoid blue light.

“We’re going to test if the same signaling that causes them to escape blue light is involved in longevity,” she said.

Eileen Chow, faculty research assistant in Giebultowicz’s lab and co-first author of the study, notes that advances in technology and medicine could work together to address the damaging effects of light if this research eventually proves applicable to humans.

“Human lifespan has increased dramatically over the past century as we’ve found ways to treat diseases, and at the same time we have been spending more and more time with artificial light,” she said. “As science looks for ways to help people be healthier as they live longer, designing a healthier spectrum of light might be a possibility, not just in terms of sleeping better but in terms of overall health.”

In the meantime, there are a few things people can do to help themselves that don’t involve sitting for hours in darkness, the researchers say. Eyeglasses with amber lenses will filter out the blue light and protect your retinas. And phones, laptops and other devices can be set to block blue emissions.

“In the future, there may be phones that auto-adjust their display based on the length of usage the phone perceives,” said lead author Trevor Nash, a 2019 OSU Honors College graduate who was a first-year undergraduate when the research began. “That kind of phone might be difficult to make, but it would probably have a big impact on health.”