Laser and Light Treatment of Acquired and Congenital Vascular Lesions

Laser and Light Treatment of Acquired and Congenital Vascular Lesions
IPL treatment of PWS
IPL devices are broadband filtered xenon flashlamps that work based on the principles of selective photothermolysis. The emission spectrum of 515–1200 nm is adjusted with the use of a series of cut-off filters, and the pulse duration ranges from approximately 0.5 to 100 msec, depending on the technology. The first commercial system, Photoderm VL (Lumenis, Yokneam, Israel) became available in 1994, and has been used to treat vascular anamolies. Another, IPL Technology (Danish Dermatologic Development [DDD] Hoersholm, Denmark) with a dual mode light filtering has also been used to treat PWS. Many other IPL system have recently been developed, and the appropriate parameters for congenital vascular lesions are being developed. The IPL has been used successfully to treat PWS (Fig. 39.7),78–80 but pulsed dye laser remains the treatment of choice.
IPL technology has also been used to treat pulsed dye laser-resistant PWS. In the study by Bjerring and associates seven of 15 patients achieved over 50% lesional lightening after four IPL treatments. Most of these patients had lesions involving the V2 dermatome (medial cheek and nose), which are relatively more difficult to lighten. Six of seven of these patients showed over 75% clearance of their PWS. A 550–950-nm filter was used with 8–30-msec pulse durations and fluences of 13–22 J/cm2 to achieve tissue purpura. The 530–750-nm filter can also be used with double 2.5-msec pulses, with a 10-msec delay and fluence of 8–10 J/cm2. Epidermal cooling was not required. Treatment resulted in immediate erythema and edema, and occasional crusting. Hypopigmentation was observed in three patients, hyperpigmentation in one patient, and epidermal atrophy in one patient.
The basics of body fat
Let’s start with the basics. Not all fat is created equal. We have two distinct types of fat in our bodies: subcutaneous fat (the kind that may roll over the waistband of your pants) and visceral fat (the stuff that lines your organs and is associated with diabetes and heart disease).
From here on out, when we refer to fat, we are talking about subcutaneous fat, as this is the type of fat that cryolipolysis targets. A recent study showed that the body’s ability to remove subcutaneous fat decreases with age, which means we are fighting an uphill battle with each birthday we celebrate.
From popsicles to freezing fat
Cryolipolysis machine — which literally translates into cold (cryo) fat (lipo) destruction (lysis) — was invented, in part, by observing what can happen when kids eat popsicles. No kidding here. The cofounders of this process were intrigued by something called “cold-induced fat necrosis” that was reported to occur after young children ate popsicles that were inadvertently left resting on the cheek for several minutes. Skin samples taken from pediatric patients like these showed inflammation in the fat, but normal overlying skin. Thus, it appeared that fat may be more sensitive to cold injury that other tissue types.
HOW DOES IT WORK?
Coolplas Fat Freeze Machine uses rounded paddles in one of four sizes to suction your skin and fat “like a vacuum,” says Roostaeian. While you sit in a reclined chair for up to two hours, cooling panels set to work crystallizing your fat cells. “It’s a mild discomfort that people seem to tolerate pretty well,” he says. “[You experience] suction and cooling sensations that eventually go numb.” In fact, the procedural setting is so relaxed that patients can bring laptops to do work, enjoy a movie, or simply nap while the machine goes to work.
WHO IS IT FOR?
Above all, emphasizes Roostaeian, CoolSculpting is “for someone who is looking for mild improvements,” explaining that it’s not designed for one-stop-shop major fat removal like liposuction. When clients come to Astarita for a consultation, she considers “their age, skin quality—will it rebound? Will it look good after volume is removed?—and how thick or pinchable their tissue is,” before approving them for treatment, because the suction panels can only treat the tissue it can access. “If someone has thick, firm tissue,” explains Astarita, “I won’t be able to give them a wow result.

WHAT ARE THE RESULTS?
“It often takes a few treatments to get to your optimum results,” says Roostaeian, who admits that a single treatment will yield very minimal change, sometimes imperceptible to clients. “One of the downsides of [CoolSculpting] is there’s a range for any one person. I’ve seen people look at before and after pictures and not be able to see the results.” All hope is not lost, however, because both experts agree that the more treatments you have, the more results you will see. What will happen eventually is an up to 25 percent fat reduction in a treatment area. “At best you get mild fat reduction—a slightly improved waistline, less bulging of any particular area that’s concerning. I would emphasize the word mild.”
WILL IT MAKE YOU LOSE WEIGHT?
“None of these devices shed pounds,” says Astarita, reminding potential patients that muscle weighs more than fat. When you’re shedding 25 percent of fat in a handful of tissue, it won’t add up to much on the scale, but, she counters, “When [you lose] what’s spilling over the top of your pants or your bra, it counts.” Her clients come to her in search of better proportions at their current weight, and may leave having dropped “one or two sizes in clothing.”

Although the mechanism of cryolipolysis is not completely understood, it is believed that vacuum suction with regulated cooling, impedes blood flow and induces crystallisation of the targeted adipose tissue with no permanent effect on the overlying dermis and epidermis. This cold induced ischaemia may promote cellular injury in adipose tissue via cellular oedema and mitochondrial free radical release. Another theory is that the initial insult of crystallisation and cold ischaemic injury is further perpetuated by ischaemia reperfusion injury, causing generation of reactive oxygen species, elevation of cytosolic calcium levels, and activation of apoptotic pathways.
Whichever the mechanism of injury, adipocytes undergo apoptosis, followed by a pronounced inflammatory response, resulting in their eventual removal from the treatment site within the following weeks. The inflammatory process sees an influx of inflammatory cells at 14 days post treatment, as adipocytes become surrounded by histiocytes, neutrophils, lymphocytes, and other mononuclear cells. At 14-30 days after treatment, macrophages and other phagocytes envelope and digest the lipid cells as part of the body’s natural response to injury. Initial concern was that cholesterol, triglycerides, low density lipoproteins (LDLs) and high density lipoproteins (HDLs), bilirubin and glucose levels were affected, however these have been shown to all stay within normal limits following the procedure.
Four weeks later, the inflammation lessens and the adipocyte volume decreases. Two to three months after treatment, the interlobular septa are distinctly thickened and the inflammatory process further subsides. Fat volume in the targeted area is apparently decreased and the septa account for the majority of the tissue volume.
Patients and treatment areas
Although all studies show reduction in every area examined, it is still unknown what areas are most responsive to cryolipolysis. Various factors may play a role in the degree of fat reduction observed after cryolipolysis. The vascularity, local cytoarchitecture, and metabolic activity of the specific fat depots in questions may play a role.
There is lack of substantial research to identify the ideal patient or even the ideal area to be treated. Given a modest (yet significant improvement of up to 25% reduction in subcutaneous fat), it is thought that the best candidates are those within their ideal weight range and those who engage in regular exercise, eat a healthy diet, have noticeable fat bulges on the trunk, are realistic in their expectations, and are willing to maintain the results of cryolipolysis with a healthy, active lifestyle.
New design Cryolipolysis is safe for all skin types, with no reported pigmentary changes, and is safe for repeated application. Ferraro et al. suggest that patients who require only small or moderate amounts of adipose tissue and cellulite removal would benefit most from cryolipolysis treatment. Contraindications include cold-induced conditions such as cryoglobunaemia, cold urticaria, and paroxysmal cold haemoglobinuria. Cryolipolysis should not be performed in treatment areas with severe varicose veins, dermatitis, or other cutaneous lesions.
HIFU Decide Guide: Which Device is for you?
High-Intensity Focused Ultrasound also known as HIFU machine is a non-surgical, non-invasive treatment procedure that tightens and lifts the skin using ultrasound energy. It is taken to be a safe and effective procedure for tightening the facial skin.
HIFU treatment procedure has loads of advantages over the traditional means of facial improvement: painless, zero incisions, scarring, and recovery time. In addition, it is far less expensive too.
Granted, HIFU is a popular means of looking better. Little wonder that there are tons of clinics and spas that offer services they claim can make you look better. However, that’s not enough reason for you to jump on the bandwagon of folks searching for ways to look better
Why Choosing The Right HIFU Device is Important?
Be sure to get a professional assessment before you start your HIFU treatment. This is because a professional and objective assessment from a specialist will help you discover the best form of treatment using the right multifunctional HIFU that is suitable for you.
Ultrasound technology is completely safe and has been used in medicine for decades. It works by contracting and shortening muscle fibers, thus causing the lifting and tightening effect that makes the skin look better.
Lifting, tightening, and fats melting are natural processes that result in a slimmer face, reduced jowl line & facial tightening. Confused about how to go about the HIFU facelift treatment in Singapore?
You will be able to decide on the best HIFU device to use when you finally decide to undertake the treatment. Let’s look at some of the most common HIFU devices in Singapore.
What is the working principle for HIFU vaginal machine?
The vaginal HIFU machine uses an noninvasive ultrasonic focusing technique to focus on the mucous membrane fibrous layer and muscle layer directly. Using ultrasonic waves as the energy source and taking advantage of its penetration and focus, the system will send out ultrasonic energy focusing in the the lamina and muscle fiber layer in a predetermined depth. A higher intensity of ultrasonic region, called focus region, is formed. In 0.1 second, the temperature of the region can reach to above 65 ℃ , so the collagen is reorganized and the normal tissue outside the focal region is undamaged. Therefore, the desired depth layer can obtain the ideal effect of collagen concentration, reorganization and regeneration. Ultimately, the mysterious effect of vagina tightening is achieved.
What is the functions for each cartridges?
1. Vagina tightening head
4.5mm heads produce the energy directly to the SMAS , make it thermal coagulation , make the SMAS tighten and lifting, improved the muscle structure from deep to shallow, make better to help the muscle layer restore elasticity and  tighten.
3.0mm head the ultrasound penetrates to the skin under the depth of 3.0mm, aim at  activating the dermal layer’s collagen, effectively enhance the effect of the consolidation of the outline, but also shrink large pores and reduce the appearance’s wrinkles.

Knowing that you are interested in 3D Hifu machine, we have listed articles on similar topics on the website for your convenience. As a professional manufacturer, we hope that this news can help you. If you are interested in learning more about the product, please feel free to contact us.3D HIFU is ultrasound energy distance of width, length and depth, which more Comprehensive, three-dimensional. Directly delivers heat energy to skin and subcutaneous tissue that can stimulate and renew the skin’s collagen and thus consequently improving the texture and reducing sagging of the skin. The high quality handle with German imported motor can be used for life. Total 8 cartridges supply treatmetn for whole body. 1.5mm is for the forehead and around the eyes, 3.0mm is for the dermis layer–face treatment, 4.5mm is for the SMAS layer–face treatment, 6.0mm/8mm/10mm/13mm/16mm for body fat layer. Every cartridge can reach 20000 lines lifetime. 

4D HIFU machine uses the power of High intensity focused ultrasound to safely lift and tighten skin. High intensity focused ultrasound is a form of energy that is significantly different than light such as IPL and Lasers or Electrical (Radio-Frequency) energy. HIFU, protects the skin surface, whilst precisely penetrating at deeper depths and higher temperatures than Radio Frequency for example, treating beyond the Dermis and Foundation layers, where structural weakening starts.
Tissue at the target point is heated to 65°C, Thermal Heat is created with the skin tissue creating both spaced ‘wounds’ and cellular friction – which in turn promotes healing, immediately contracts collagen and stimulates a rapid production. Over the next 90-180 days, the wound-healing response stimulates long-term tissue and leads to further lifting and tightening, with results that can last years.
4D HIFU also helps to improve the tone and all the features of your face such as your eyes, cheeks, mouth, chin and skin also making it a viable alternative to Botox, with the benefit of being able to maintain facial expression. Excellent for post surgical face lift to maintain the lift and treat blood stasis, scarring, and numbness. 
This mode could minimize treatment time achieves the great anti-aging result. It will be leading the new treading in the market, and help you to expand your business.

A Beginner’s Guide To Acoustic Treatment

[font=”Open Sans”, Helvetica, Arial, sans-serif]An account of an acoustic newbie’s journey from bare walls to a well‑balanced, sonically pleasant space.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]The physics of the propagation of sound is immensely complicated, and when the assortment of materials that make up the walls, floors and ceiling (plus any windows, doors and furniture) are added to the equation, it’s very difficult to predict what will happen to sound waves once they’ve left their source. What’s more, every room is different, and it’s not just the dimensions that will dictate how the room will sound… Imagine two rooms of the same shape and size. One has two‑metre-thick concrete walls, and the other a single‑layer plasterboard stud-wall. Even with those brief, albeit extreme descriptions, you probably know already that the two rooms will sound very different. Add in the multitude of room shapes, sizes, wall‑construction methods and surfaces found in home studios, and it becomes impossible to provide a one-size-fits-all guide to acoustic panel treatment.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]The subject of acoustics is regularly discussed in SOS, but plenty of readers still ask for the subject to be covered from a much more basic starting point. What follows is a look at installing acoustic treatment from a complete beginner’s perspective: some basic, essential information, along with a bit of advice from acoustics professionals that should give you the confidence to get started. I’ll follow this up by taking you step by step through my own recent experience of treating a room.[/font]
Why Bother With Acoustic Treatment?
[font=”Open Sans”, Helvetica, Arial, sans-serif]Untreated rooms have an uneven frequency response, which means that any mixing decisions you make are being based on a sound that is ‘coloured’, because you can’t accurately hear what’s being played. In short, you can’t possibly tell how your mix will sound when played back anywhere else. It isn’t just an issue for mixing, though, because any recordings you make of acoustic instruments will bear all the hallmarks of the space in which you record them. That may be a good thing if the space in question is Ocean Way or SARM West, but probably preposterously bad if it’s your living room or bedroom. So, if you want your mixes to transfer well, and your recordings to be free of room ‘honk’, you need to pay attention to the acoustic properties of your environment — no matter how good the gear you’re using.[/font]
First Things First
[font=”Open Sans”, Helvetica, Arial, sans-serif]The first thing to grasp is the outcome you want to achieve. It’s a common misconception that acoustic treatment with acoustic ceilings or acoustic baffles should kill all reverberation, and that you want a room covered floor‑to‑ceiling with foam tiles: this isn’t what you’re aiming for. You also need to bear in mind the limitations imposed by space and budget: most home studios are small in comparison with the Abbey Roads and AIR Lyndhursts of this world, and many home‑studio owners simply don’t have the funds for bespoke treatment solutions.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]So what is the aim? Andy Munro, acoustic design specialist, remarks, “acoustic design is the science that restores a neutral sound balance”. Applying that science means interfering with the path of sound to control the sound energy. Jorge Castro, chief acoustician at Vicoustic, says that “in the case of affordable treatment, we need to control the energy of the sound first. Then we can take care of the sound quality. With small spaces, bass frequencies are always a problem, and we should control the low frequencies as much as we can.” In fact, he continues, “In small rooms, I’ve never heard people saying they have too much absorption of low frequencies.”[/font]
Absorption & Diffusion: What, Where, Why?
[font=”Open Sans”, Helvetica, Arial, sans-serif]To achieve the right balance, there are two main approaches: absorption and diffusion. Products that have absorptive properties include foam and rigid mineral-wool (see the ‘DIY & Rockwool’ box), and they ‘soak up’ the sound energy, turning it into heat, through friction. Most effective on high‑frequencies, absorption is essential for reducing flutter echoes and for taming bright‑sounding or ‘ringy’ rooms. Bass trapping is also a type of absorption, but is specifically designed to absorb low‑frequency energy. A clever combination of soft, hard, thick and thin materials, including air, is used to make the most efficient bass trap, and an empty gap between the wall and the back of the trap helps to make it even more effective.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]Diffusion is the scattering of sound energy using multi‑faceted surfaces. Diffusers are commonly made of wood, plastic, or even polystyrene. Jorge Castro explains: “diffusion helps in energy control and improves the sound quality in frequencies throughout the middle and high range of the spectrum, and also improves sweet‑spot image.” The ‘sweet spot’ is the place between the speakers where you should be sitting to get the best stereo image (imagine that your head and the two speakers form an equilateral triangle). That pretty much concludes the theory: now for the practice![/font]
Getting Started
[font=”Open Sans”, Helvetica, Arial, sans-serif]Before undertaking this project, I’d read plenty about acoustics, but had never attempted to properly treat a room myself: the nearest I’d come was propping foam panels against the walls to tame flutter in the spare‑room‑cum‑studio of my rented house. I hadn’t been able to glue or screw anything to the walls, for fear of incurring my landlord’s wrath, and the thought of retouching the paintwork after tearing strips of self‑adhesive velcro pained me too! So this was very much a learning experience.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]The space in question included an area that would provide a reasonable‑sized live room, and another that would serve as a small control room, and although both were important, I really wanted to get the performance space right. I decided that I’d buy commercially available panels, because I simply didn’t have the time, space or inclination for the DIY option. Most manufacturers of acoustic products also offer a consultation service, and they often have free on‑line calculators to help you decide on a suitable treatment option, too, so even if you choose the DIY route this can be a sensible place to start, and fabric acoustic panels are also available.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]I chose to get my treatment from Vicoustic, a company relatively new to the UK acoustic‑treatment market who make a range of products for studios and home theatres. I told them that, as this was the only live room for a small project studio, it needed to be quite versatile, with both a ‘dead’ corner for dry recordings and a more ambient space to liven up acoustic recordings where needed. I’d expected a solution with almost complete wall coverage, foam panels and diffusers covering every square inch, but Vicoustic came back with a plan that surprised me, which suggested that total coverage wasn’t necessary.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]In fact, Jorge says that the typical home studio needs only between 30 and 40 percent coverage to adequately treat it. So don’t go over the top: remember that we’re trying to control the energy, or “restore the natural sound balance,” and not to kill the sound completely.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]As for the proportion of diffusion to absorption, Jorge says, “some believe it should be 50 percent absorption and 50 percent diffusion. In the home studio, because of budget and space constraints, the actual proportion can vary considerably.”[/font]
Planning
[font=”Open Sans”, Helvetica, Arial, sans-serif]So, you’ve decided on your acoustic foam treatment, you’ve had it delivered, and it’s piled in the middle of the room. The next step is sticking it up on the walls, right? Well yes… but you also want to make sure that it goes in the right place, partly to optimise its acoustic performance, and partly because you don’t want it to look like it’s been put up by a two‑year old! As a first‑timer, I found it useful to have the 3D drawings Vicoustic had supplied, as they enabled me to plan precisely where each panel would go. You can create a computer‑generated version of your room yourself using a freeware 3D drawing programme such as Google Sketchup (http://sketchup.google.com). This may seem a bit over the top (sketches on the back of an envelope would do the job), but it can provide a useful guide to print out and use like a map during installation. What’s more, you can plan the look of a room, moving tiles and panels around on the computer instead of having to rip them off the wall if they look silly.[/font]
Measure Twice, Stick Once
[font=”Open Sans”, Helvetica, Arial, sans-serif]With my ‘map’ in hand, it was time to mark up the walls. The Vicoustic plans showed the panels equally spaced along the walls, but without any dimensions or measurements to indicate how to space the tiles, so I measured the whole room and planned the position of all the panels supplied. A quick and easy formula for plotting the position of a row of equally spaced panels soon emerged. To calculate the distance between each panel, and between the end panels and the walls, you just measure the length of the wall, subtract the total width of all the panels to be fixed to it, then divide that figure by the number of gaps between panels (or by the number of panels plus one). Marking up is then a cinch, but to get things looking good, you’ll need to mark the corner points and will require a spirit level and a spare pair of hands. O[font=”Open Sans”, Helvetica, Arial, sans-serif]nce plotted and marked, it’s also a good idea to double‑check that you have the same number of actual panels as you have on your plan![/font][/font]

Stick ‘Em Up!
[font=”Open Sans”, Helvetica, Arial, sans-serif]With the planning done, it’s time to stick the panels to the walls and ceiling. The way you do this depends on the type of treatment you’re applying. Large, framed panels will come with brackets and (hopefully) sturdy fixings, whereas foam‑based tiles will need to be glued, using an aerosol‑based product or a tube of paste‑like glue that needs a skeleton gun. Spray‑mounting can often give less than satisfactory results, so I was glad to discover that the Vicoustic delivery included the tube variety. With just two tubes supplied, though, I soon had to resort to alternatives, and found that the sticky gunk used to fix mirrors to walls worked exceptionally well.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]To prevent the glue squidging out from the sides of the panels, I piped the glue on no less than an inch from the guide line on the wall and on the back of the panel itself, in different patterns, to increase the adhesion. With this kind of glue, I found that it would begin to set in about a minute, allowing just enough time to pull the panel off and turn it if it was the wrong way up. When sticking panels to the ceiling, I took the same approach. It was a textured ceiling, which called for lots of glue and a firm hand to seat the panels: again, it’s useful if you can get a friend to lend a hand.[/font]
Hearing The Result
[font=”Open Sans”, Helvetica, Arial, sans-serif]Once in place, the Vicoustic treatment worked very well. The main part of the room is now nicely controlled, if a bit on the ‘live’ side, and the diffusers ensure excellent intelligibility of speech: a sure‑fire sign of good acoustic control. I had a few spare corner traps, which were put into the dry corner, to make it even more ‘dead’, and it will be easy to add a few smaller foam tiles to dampen the sound further if it’s found to be too ‘roomy’ further down the line.[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]Having tried some recordings in the room, I’m happy to say that excellent sound barrier can be achieved between acoustic instruments and vocals by using the different areas of the room. Because the sound inside the room is controlled, the ambience can be used to good effect if a roomy sound is desired on the recording.[/font]
Ultimate Control
[font=”Open Sans”, Helvetica, Arial, sans-serif]So far, I’ve only addressed the dedicated live/recording space, and most home studios are single rooms, with both the monitoring and performance areas in the same space, so I asked Andy Munro to explain how to approach treating such a space. “The best approach,” he said, “is to sketch the room out, then divide each dimension into thirds. If the mixing position is on a third ratio, and so are the speakers, they will not stand on any of the half or quarter ‘standing’ wavelengths that cause a peak or trough in the bass [see the ‘Standing Waves’ box for more information]. The result will be a smoother sound, with fewer problems when the acoustic absorption and sound barrier is added. Ironically, most professional rooms are set up about the centre line, which tends to result in a ‘hole’ at certain frequencies.”[/font]
[font=”Open Sans”, Helvetica, Arial, sans-serif]Also important in monitoring rooms is the control of early reflections. When a speaker cone is driven, it disperses acoustic energy to the listener’s ears directly, and also to the walls and ceiling of the room, and the best example may be acoustic diffuser. Uncontrolled, these early reflections bounce back into the room and reach the listener a few milliseconds later than the direct sounds, because of the additional distance they’ve had to travel. Unless in a large room, this delay is not perceivable as a different sound; instead it disturbs the phase, and therefore the clarity, of the sound. To keep early reflections on a tight leash, the ‘mirror points’ of the room should be identified and treated. To do this, sit in the listening position and ‘guesstimate’ where a mirror would have to be placed to enable you to see each monitor cone from the sweet spot. Then apply absorption to these points. A ‘ceiling cloud’ can be positioned in a similar way, to control vertical reflections.[/font]
Conclusion
[font=”Open Sans”, Helvetica, Arial, sans-serif]No matter how much you spend on instruments, amps, speakers and recording gear, you still need to pay attention to the space in which you use them. The treatment of home studios is tricky, because of their size and the construction materials used, not to mention the budget of the average home‑studio owner. It’s impossible to get a ‘pro-studio sound’ from a space that’s built as a spare bedroom, mainly due to the laws of physics, but also because ‘proper’ studios might have big bucks spent on acoustic design with soundproof materials. But if you can get your head around what you’re trying to achieve, you can still make such a space perfectly usable, with only a small amount of money, some forward planning and a little bit of knowledge.[/font]

Dazed and Mesmerist Touch treatment

i was looking at the mesmerist occult class and they have an ability called Touch treatment, its like a paladins lay on hands for psychics that only cures conditions listed on the ability, one of the moderate ailments is dazed and dazed states that you “dont takes actions normally” then says you take no actions for a round, the mesmerist is able to as a swift action remove ailments from itself. so my question is, can the mesmerist use a swift action to remove the dazed condition from itself?

By RAW, does jumping in combat need any special treatment?

Jumping is a special kind of movement. There doesn’t seem to be special combat rules for jumping, so it behaves the same as walking. Is any special consideration needed when jumping in combat?

These prompts may help you form a response:

  1. Do you have to declare how far you are going to jump?
  2. Can you attack mid jump?
  3. Can you end your turn mid air?
  4. How does jumping over an enemy interact with attack of opportunity?
  5. How does jumping vertically higher than 5ft + an enemies height interact with attack of opportunity?
  6. How do actions such as Disengage or Dash interact with jumping?
  7. Are there any other quirks?

Please note I am asking about RAW only, and I understand the consequences of doing so. No frame challenges please. Do not assume this question is useless. Assume I want to jump, have considered alternates, and that it is useful to me.

Alignment of Very Powerful Beings – How Relevant is Their Treatment of Lower Beings?

Say you have a very powerful being, who carries out deeds on a cosmic level. They are good in the sense that they save the universe from massive threats. However, they do not save individual human lives, even when they are very capable of doing so, and aware of the issue.

For example, they come to a planet to do a part of an important task. While there, they see a village of humans being slaughtered. They do not intervene, despite being very capable of saving all of the humans. It’s not even that they are torn. It’s just that an individual human life isn’t very significant. The task they set out to do is much more important, and may save the universe as a whole.

So I’m wondering about such a character’s alignment. Would they be good? They go out of there way to do good deeds. There is no denying that just about everything meaningful they do is good. Saving the universe or large swathes of it from extradimensional threats and whatnot. But some say that a respect for all sentient life is required for good characters, and that a good character would not ignore people in need who they could easily save.

On the other hand, the main reason for this is because humans are just so far beneath them. A good human character might bypass a colony of ants that is in trouble on their way to do some other good deed. So in a sense this is similar.

What do you think? Would such a character be good or neutral?

Can homomorphic treatment lead to disclose the context, and so break confidentiality?

Example: I want company X to perform an analysis on the data I output from a campaign of tensile testing. I send them encrypted data and ask them to fit a function between deformation and constraint (I say to X, my data is u and v), to obtain the response of my material to solicitation for future calculation

What prevents X from guessing what they are calculating, while the function they will fit is a physical response a technician will probably recognise?

(I know my example is bad cause this is a simple operation, with no need to outsource, but I have no better one)

Analyzing patient treatment data using Pandas

I work in the population health industry and get contracts from commercial companies to conduct research on their products. This is the general code to identify target patient groups from a provincial datasets, including DAD (hospital discharge), PC (physician claims), NACRS (emergency room visit), PIN (drug dispensation), and REG (provincial registry). Same patients can have multiple rows in each of the databases. For example, if a patient was hospitalized 3 times, s/he will show up as three separate rows in DAD data. The code does the followings:

  1. Import data from csv files into individual Pandas dataframes (df’s)
  2. Then it goes through some initial data cleaning and processing (such as random sampling, date formatting, calling additional reference information (such as icd code for study condition)
  3. Under the section 1) Identify patients for case defn'n #1, a series of steps have been done to label (as tags) each of the relevant data and filtering based on these tags. Datasets are linked together to see if a particular patient fulfills the diagnostic code requirement.
  4. Information also needs to be aggregated by unique patient level via the pivot_table function to summarize by unique patients
  5. At the end, the final patient dataframe is saved into local directory, and analytic results are printed
  6. I also made my own modules feature_tagger to house some of the more frequently-used functions away from this main code
# Overall steps: # 1) Patient defintiion: Had a ICD code and a procedure code within a time period # 2) Output: A list of PHN_ENC of included patients; corresponding index date     # .. 'CaseDefn1_PatientDict_FINAL.txt'     # .. 'CaseDefn1_PatientDf_FINAL.csv' # 3) Results: Analytic results # ----------------------------------------------------------------------------------------------------------  import pandas as pd import datetime import random import feature_tagger.feature_tagger as ft import data_descriptor.data_descriptor as dd import data_transformer.data_transformer as dt import var_creator.var_creator as vc  # Unrestrict pandas' output display pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 120)  # Control panel save_file_switch = False # WARNING: will overwrite existing when == True df_subsampling_switch = False # WARNING: make to sure turn off for final results edge_date_inclusion = True # whether to include the last date in the range of inclusion criteria testing_printout_switch = False result_printout_switch = True done_switch = True df_subsampling_n = 15000 random_seed = 888  # Instantiate objects ft_obj = ft.Tagger() dt_obj = dt.Data_Transformer()  # Import data loc = 'office' if loc == 'office':     directory = r'E:\My_Working_Primary\Projects\Data_Analysis\' elif loc == 'home':     directory = r'C:\Users\MyStuff\Dropbox\Projects\Data_Analysis\' else: pass  refDataDir = r'_Data\RefData\' realDataDir = r'_Data\RealData\' resultDir = r'_Results\'  file_dad = 'Prepped_DAD_Data.csv' file_pc = 'Prepped_PC_Data.csv' file_nacrs = 'Prepped_NACRS_Data.csv' file_pin = 'Prepped_PIN_Data.csv' file_reg = 'Prepped_REG_Data.csv'  df_dad = pd.read_csv(directory+realDataDir+file_dad, dtype={'PHN_ENC': str}, encoding='utf-8', low_memory=False) df_pc = pd.read_csv(directory+realDataDir+file_pc, dtype={'PHN_ENC': str}, encoding='utf-8', low_memory=False) df_nacrs = pd.read_csv(directory+realDataDir+file_nacrs, dtype={'PHN_ENC': str}, encoding='utf-8', low_memory=False) df_pin = pd.read_csv(directory+realDataDir+file_pin, dtype={'PHN_ENC': str}, encoding='utf-8', low_memory=False) df_reg = pd.read_csv(directory+realDataDir+file_reg, dtype={'PHN_ENC': str}, encoding='utf-8', low_memory=False)  # Create random sampling of df's to run codes faster if df_subsampling_switch==True:     if (df_subsampling_n>len(df_dad))|(df_subsampling_n>len(df_pc))|(df_subsampling_n>len(df_nacrs))|(df_subsampling_n>len(df_pin)):         print ('Warning: Specified subsample size is larger than the total no. of row of some of the dataset,')         print ('As a result, resampling with replacement will be done to reach specified subsample size.')     df_dad = dt_obj.random_n(df_dad, n=df_subsampling_n, on_switch=df_subsampling_switch, random_state=random_seed)     df_pc = dt_obj.random_n(df_pc, n=df_subsampling_n, on_switch=df_subsampling_switch, random_state=random_seed)     df_nacrs = dt_obj.random_n(df_nacrs, n=df_subsampling_n, on_switch=df_subsampling_switch, random_state=random_seed)     df_pin = dt_obj.random_n(df_pin, n=df_subsampling_n, on_switch=df_subsampling_switch, random_state=random_seed)  # Format variable type df_dad['ADMIT_DATE'] = pd.to_datetime(df_dad['ADMIT_DATE'], format='%Y-%m-%d') df_dad['DIS_DATE'] = pd.to_datetime(df_dad['DIS_DATE'], format='%Y-%m-%d') df_pc['SE_END_DATE'] = pd.to_datetime(df_pc['SE_END_DATE'], format='%Y-%m-%d') df_pc['SE_START_DATE'] = pd.to_datetime(df_pc['SE_START_DATE'], format='%Y-%m-%d') df_nacrs['ARRIVE_DATE'] = pd.to_datetime(df_nacrs['ARRIVE_DATE'], format='%Y-%m-%d') df_pin['DSPN_DATE'] = pd.to_datetime(df_pin['DSPN_DATE'], format='%Y-%m-%d') df_reg['PERS_REAP_END_RSN_DATE'] = pd.to_datetime(df_reg['PERS_REAP_END_RSN_DATE'], format='%Y-%m-%d')  # Import reference codes file_rxCode = '_InStudyCodes_ATC&DIN.csv' file_icdCode = '_InStudyCodes_DxICD.csv' file_serviceCode = '_InStudyCodes_ServiceCode.csv'  df_rxCode = pd.read_csv(directory+refDataDir+file_rxCode, dtype={'ICD_9': str}, encoding='utf-8', low_memory=False) df_icdCode = pd.read_csv(directory+refDataDir+file_icdCode, encoding='utf-8', low_memory=False) df_serviceCode = pd.read_csv(directory+refDataDir+file_serviceCode, encoding='utf-8', low_memory=False)  # Defining study's constant variables inclusion_start_date = datetime.datetime(2017, 4, 1, 00, 00, 00)  inclusion_end_date = datetime.datetime(2018, 3, 31, 23, 59, 59)  sp_serviceCode_dict = {df_serviceCode['Short_Desc'][0]:df_serviceCode['Health_Service_Code'][0]} sp_serviceCode_val = sp_serviceCode_dict['ABC injection']  sp_dxCode_dict = {'DIABETES_ICD9': df_icdCode['ICD_9'][0], 'DIABETES_ICD10': df_icdCode['ICD_10'][0]} sp_dxCode_val_icd9 = sp_dxCode_dict['DIABETES_ICD9'] sp_dxCode_val_icd10 = sp_dxCode_dict['DIABETES_ICD10']  # ----------------------------------------------------------------------------------------------------------  # 1) Identify patients for case def'n #1. # Step 1 - Aged between 18 and 100 years old on the index date # Step 2 - Had at least 1 recorded ICD diagnostic code based on physician visit (ICD-9-CA=9999 in PC) or      # hospitalization (ICD-10-CA=G9999 in DAD) during the inclusion period # Step 3.1 - Had at least 1 specific procedure code (99.999O) during      # the inclusion period (Note: earliest ABC injection code date is the Index date) # Step 3.2 - Construct index date # Step 4 - Registered as a valid Alberta resident for 2 years before the index date and 1 year after the      # index date (determined from PR)  # 1.1) Get age at each service, then delete rows with age falling out of 18-100 range df_dad_ageTrimmed = df_dad.copy() df_dad_ageTrimmed = df_dad_ageTrimmed[(df_dad_ageTrimmed['AGE']>=18) & (df_dad_ageTrimmed['AGE']<=100)]  df_pc_ageTrimmed = df_pc.copy() df_pc_ageTrimmed = df_pc_ageTrimmed[(df_pc_ageTrimmed['AGE']>=18) & (df_pc_ageTrimmed['AGE']<=100)]  # 1.2) Tag appropriate date within sp range > tag DIABETES code > combine tags df_dad_ageTrimmed['DAD_DATE_TAG'] = ft_obj.date_range_tagger(df_dad_ageTrimmed, 'ADMIT_DATE',      start_date_range=inclusion_start_date, end_date_range=inclusion_end_date, edge_date_inclusion=     edge_date_inclusion) df_dad_ageTrimmed['DAD_ICD_TAG'] = ft_obj.multi_var_cond_tagger(df_dad_ageTrimmed, repeat_var_base_name='DXCODE',      repeat_var_start=1, repeat_var_end=25, cond_list=[sp_dxCode_val_icd10]) df_dad_ageTrimmed['DAD_DATE_ICD_TAG'] = ft_obj.summing_all_tagger(df_dad_ageTrimmed, tag_var_list=['DAD_DATE_TAG',      'DAD_ICD_TAG'])  df_pc_ageTrimmed['PC_DATE_TAG'] = ft_obj.date_range_tagger(df_pc_ageTrimmed, 'SE_END_DATE',      start_date_range=inclusion_start_date, end_date_range=inclusion_end_date, edge_date_inclusion=     edge_date_inclusion) df_pc_ageTrimmed['PC_ICD_TAG'] = ft_obj.multi_var_cond_tagger(df_pc_ageTrimmed, repeat_var_base_name='HLTH_DX_ICD9X_CODE_',      repeat_var_start=1, repeat_var_end=3, cond_list=[str(sp_dxCode_val_icd9)]) df_pc_ageTrimmed['PC_DATE_ICD_TAG'] = ft_obj.summing_all_tagger(df_pc_ageTrimmed, tag_var_list=['PC_DATE_TAG',      'PC_ICD_TAG'])  # Output a list of all patients PHN_ENC who satisfy the Date and DIABETES code criteria df_dad_ageDateICDtrimmed = df_dad_ageTrimmed[df_dad_ageTrimmed['DAD_DATE_ICD_TAG']==1] df_pc_ageDateICDtrimmed = df_pc_ageTrimmed[df_pc_ageTrimmed['PC_DATE_ICD_TAG']==1]  dad_patientList_diabetes_Code = df_dad_ageDateICDtrimmed['PHN_ENC'].unique().tolist() pc_patientList_diabetes_Code = df_pc_ageDateICDtrimmed['PHN_ENC'].unique().tolist() dad_pc_patientList_diabetes_Code = list(set(dad_patientList_diabetes_Code)|set(pc_patientList_diabetes_Code)) dad_pc_patientList_diabetes_Code.sort()  # 1.3.1) Tag appropriate date within sp range > tag ABC injection code > combine tags df_pc_ageTrimmed['PC_PROC_TAG'] = df_pc_ageTrimmed['ABC_INJECT'] df_pc_ageTrimmed['PC_DATE_PROC_TAG'] = ft_obj.summing_all_tagger(df_pc_ageTrimmed, tag_var_list=['PC_DATE_TAG',      'PC_PROC_TAG']) df_pc_ageDateProcTrimmed = df_pc_ageTrimmed[df_pc_ageTrimmed['PC_DATE_PROC_TAG']==1]  pc_patientList_procCode = df_pc_ageDateProcTrimmed['PHN_ENC'].unique().tolist() dad_pc_patientList_diabetes_NprocCode = list(set(dad_pc_patientList_diabetes_Code)&set(pc_patientList_procCode)) dad_pc_patientList_diabetes_NprocCode.sort()  # 1.3.2) Find Index date df_pc_ageDateProcTrimmed_pivot = pd.pivot_table(df_pc_ageDateProcTrimmed, index=['PHN_ENC'],      values=['SE_END_DATE', 'AGE', 'SEX', 'RURAL'], aggfunc={'SE_END_DATE':np.min, 'AGE':np.min,     'SEX':'first', 'RURAL':'first'}) df_pc_ageDateProcTrimmed_pivot = pd.DataFrame(df_pc_ageDateProcTrimmed_pivot.to_records()) df_pc_ageDateProcTrimmed_pivot = df_pc_ageDateProcTrimmed_pivot.rename(columns={'SE_END_DATE':'INDEX_DT'})  # 1.4) Filter by valid registry # Create a list variable (based on index date) to indicate which fiscal years need to be valid according to     # the required 2 years before index and 1 year after index date, in df_pc_ageDateProcTrimmed_pivot def extract_needed_fiscal_years(row): # extract 2 years before and 1 year after index date     if int(row['INDEX_DT'].month) >= 4:         index_yr = int(row['INDEX_DT'].year)+1     else:          index_yr = int(row['INDEX_DT'].year)     first_yr = index_yr-2     four_yrs_str = str(first_yr)+','+str(first_yr+1)+','+str(first_yr+2)+','+str(first_yr+3)     return four_yrs_str  df_temp = df_pc_ageDateProcTrimmed_pivot.copy() df_temp['FYE_NEEDED'] = df_temp.apply(extract_needed_fiscal_years, axis=1) df_temp['FYE_NEEDED'] = df_temp['FYE_NEEDED'].apply(lambda x: x[0:].split(',')) # from whole string to list of string items df_temp['FYE_NEEDED'] = df_temp['FYE_NEEDED'].apply(lambda x: [int(i) for i in x]) # from list of string items to list of int items  # Create a list variable to indicate the active fiscal year, in df_reg df_reg['FYE_ACTIVE'] = np.where(df_reg['ACTIVE_COVERAGE']==1, df_reg['FYE'], np.nan) df_reg_agg = df_reg.groupby(by='PHN_ENC').agg({'FYE_ACTIVE':lambda x: list(x)}) df_reg_agg = df_reg_agg.reset_index() df_reg_agg['FYE_ACTIVE'] = df_reg_agg['FYE_ACTIVE'].apply(lambda x: [i for i in x if ~np.isnan(i)]) # remove float nan df_reg_agg['FYE_ACTIVE'] = df_reg_agg['FYE_ACTIVE'].apply(lambda x: [int(i) for i in x]) # convert float to int  # Merge df's and create tag, if active years do not cover all the required fiscal year, exclude patients # Create inclusion/exclusion patient list to apply to obtain patient cohort based on case def'n #1 df_temp_v2 = df_temp.merge(df_reg_agg, on='PHN_ENC', how='left') df_temp_v2_trimmed = df_temp_v2[(df_temp_v2['FYE_NEEDED'].notnull())&(df_temp_v2['FYE_ACTIVE'].notnull())] # Remove rows with missing on either variables  def compare_list_elements_btw_cols(row):     if set(row['FYE_NEEDED']).issubset(row['FYE_ACTIVE']):         return 1     else:         return 0  df_temp_v2_trimmed['VALID_REG'] = df_temp_v2_trimmed.apply(compare_list_elements_btw_cols, axis=1) df_temp_v2_trimmed_v2 = df_temp_v2_trimmed[df_temp_v2_trimmed['VALID_REG']==1] reg_patientList = df_temp_v2_trimmed_v2['PHN_ENC'].unique().tolist()  # Apply inclusion/exclusion patient list (from REG) to find final patients # Obtain final patient list df_final_defn1 = df_pc_ageDateProcTrimmed_pivot.merge(df_temp_v2_trimmed_v2, on='PHN_ENC', how='inner') df_final_defn1 = df_final_defn1[['PHN_ENC', 'AGE_x', 'SEX_x', 'RURAL_x', 'INDEX_DT_x']] df_final_defn1 = df_final_defn1.rename(columns={'AGE_x':'AGE', 'SEX_x':'SEX', 'RURAL_x':'RURAL', 'INDEX_DT_x':'INDEX_DT',}) df_final_defn1['PREINDEX_1Yr'] = (df_final_defn1['INDEX_DT']-pd.Timedelta(days=364)) # 364 because index date is counted as one pre-index date df_final_defn1['PREINDEX_2Yr'] = (df_final_defn1['INDEX_DT']-pd.Timedelta(days=729)) # 729 because index date is counted as one pre-index date df_final_defn1['POSTINDEX_1Yr'] = (df_final_defn1['INDEX_DT']+pd.Timedelta(days=364))  list_final_defn1 = df_final_defn1['PHN_ENC'].unique().tolist() dict_final_defn1 = {'Final unique patients of case definition #1':list_final_defn1}  # Additional ask (later on) # How: Create INDEX_DT_FIS_YR (index date fiscal year) by mapping INDEX_DT to fiscal year def index_date_fiscal_year(row):     if ((row['INDEX_DT'] >= datetime.datetime(2015, 4, 1, 00, 00, 00)) &         (row['INDEX_DT'] < datetime.datetime(2016, 4, 1, 00, 00, 00))):         return '2015/2016'     elif ((row['INDEX_DT'] >= datetime.datetime(2016, 4, 1, 00, 00, 00)) &         (row['INDEX_DT'] < datetime.datetime(2017, 4, 1, 00, 00, 00))):         return '2016/2017'     else:         return 'Potential error'  df_final_defn1['INDEX_DT_FIS_YR'] = df_final_defn1.apply(index_date_fiscal_year, axis=1)  # 2) Output final patient list for future access # WARNING: will overwrite existing if save_file_switch == True:     if df_subsampling_switch == True:         f = open(directory+resultDir+'_CaseDefn1_PatientDict_Subsample.txt',"w")         f.write(str(dict_final_defn1)+',')         f.close()         df_final_defn1.to_csv(directory+resultDir+'_CaseDefn1_PatientDf_Subsample.csv', sep=',', encoding='utf-8')     elif df_subsampling_switch == False:         f = open(directory+resultDir+'CaseDefn1_PatientDict_FINAL.txt',"w")         f.write(str(dict_final_defn1)+',')         f.close()         df_final_defn1.to_csv(directory+resultDir+'CaseDefn1_PatientDf_FINAL.csv', sep=',', encoding='utf-8')  # 3) Results: Analytic results if result_printout_switch == True:     print ('Unique PHN_ENC N, (aged 18 to 100 during inclusion period) from DAD:')     print (df_dad_ageTrimmed['PHN_ENC'].nunique())      print ('Unique PHN_ENC N, (aged 18 to 100 during inclusion period) from PC:')     print (df_pc_ageTrimmed['PHN_ENC'].nunique())      print ('Unique PHN_ENC N, (aged 18 to 100 during inclusion period) from DAD or PC:')     dd_obj = dd.Data_Comparator(df_dad_ageTrimmed, df_pc_ageTrimmed, 'PHN_ENC')     print (dd_obj.unique_n_union())      print ('Unique PHN_ENC N, (aged 18 to 100) and (had DIABETES code during inclusion period) from DAD:')     print (df_dad_ageDateICDtrimmed['PHN_ENC'].nunique())      print ('Unique PHN_ENC N, (aged 18 to 100) and (had DIABETES code during inclusion period) from PC:')     print (df_pc_ageDateICDtrimmed['PHN_ENC'].nunique())      print ('Unique PHN_ENC N, (aged 18 to 100) and (had DIABETES code during inclusion period) from DAD or PC:')     print (len(dad_pc_patientList_diabetes_Code))      print ('Unique PHN_ENC N, (aged 18 to 100) and (had DIABETES code during inclusion period)\ and (had ABC injection code) from DAD and PC:')     print (df_pc_ageDateProcTrimmed_pivot['PHN_ENC'].nunique())      print ('Unique PHN_ENC N, (aged 18 to 1005) and (had DIABETES code during inclusion period)\ and (had ABC injection code) and (had AB resident around index date) from DAD, PC, and REG [Case Def #1]:')     print (df_final_defn1['PHN_ENC'].nunique())      # Additional analytic ask (later on)     print ('Patient N by index date as corresponding fiscal year:')     print (df_final_defn1['INDEX_DT_FIS_YR'].value_counts())  if done_switch == True:     ctypes.windll.user32.MessageBoxA(0, b'Hello there', b'Program done.', 3) 

My questions are:

  • This is a code for a specific project, other projects from other companies while are the exactly the same, they usually follow similar overall steps including cleaning data, linking data, creating tags, filtering tags, aggregating data, saving files, and producing data. How can I refactor my code to be maintainable within this specific project, as well as reusable across similar projects?
  • Many times, once I have run the code and produce the results, clients may come back to ask for additional follow-up information (i.e., the ones under # Additional ask (later on)). How can I deal with additional asks more effectively with maintainability and expandability in mind?
  • Any areas I can try using some design patterns?
  • Any other suggestions on how I can write better python code are more than welcome.

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How to choose a prestigious acne treatment?