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Our bestselling meal replacement shake for weight loss, on the go!
Vanilla Ice Cream / 10 Sachets - $26.99 Milk Chocolate / 10 Sachets - $26.99 Chocolate Mint / 10 Sachets - $26.99 Strawberry Milkshake / 10 Sachets - $26.99 Banana Split / 10 Sachets - $26.99 Salted Caramel / 10 Sachets - $26.99 White Chocolate & Raspberry / 10 Sachets - $26.99 Peanut Butter / 10 Sachets - $26.99 Cookies & Cream / 10 Sachets - $26.99
The Slender Blend™ is our premium, best-selling weight loss shake. This high protein, low calorie versatile protein shake can be used as a Meal replacement shake, post-workout shakes and even make for a delicious low-calorie snack. Not only is it packed with protein to aid in the maintenance of muscle, it also has low sugar and fat content!
Boosted with pre & probiotics, vitamin C and essential B vitamins, the Slender Blend additionally contributes to the upkeep of your immune system and keeps your hair, skin and nails looking radiant! When combined with a varied and balanced diet the Slender Blend™ helps you to achieve your weight loss goals.
High protein to aid the maintenance and development of lean muscle mass.
Contains our Protein World Thermogenic Blend which aids fat metabolism.
Contains our very own digestive enzyme blend as well as pre and probiotics to promote a healthy gut microbiome.
Packed with a multitude of vitamins and minerals to support overall health.
Our single servings sachets are handy to take wherever you go!
Meal Replacement: Add one sachet to 400ml of almond milk and shake well! Replace 1 or 2 meals per day, choose which meal based on your routine and preference.
Snack/Post Workout: Add 40g (4 scoops) to 400ml of water and shake well!
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Inulin, Natural Flavourings, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophilus).
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Cocoa Powder, Inulin, Natural Flavourings, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophillus).
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Inulin, Natural Flavourings, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Red Beetroot Powder (natural colour), Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophillus).
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Inulin, Natural Flavourings, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophillus).
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Inulin, Natural Flavourings, Salt, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophillus).
White Chocolate & Raspberry
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Inulin, Natural Flavourings, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Red Beetroot Powder (natural colour), Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophillus).
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Peanut Flour, Natural Flavourings, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Inulin, Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Sodium Chloride, Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophillus).
Fudge Sundae
Whey Protein Concentrate (milk), Skimmed Milk Powder (milk), Inulin, Xanthan Gum (thickener), Protein World Vitamin & Mineral Blend [Potassium Hydrogen Orthophosphate, Vitamin C (Ascorbic Acid), Vitamin K2 (Menaquinone), Zinc Citrate, Vitamin A (Beta Carotene), Niacin (Nicotinamide), Carbonyl Iron, Vitamin D (Cholecalciferol), Manganese Gluconate, Vitamin B6 (Pyridoxine Hydrochloride), Vitamin B1 (Thiamine Hydrochloride), Chromium Picolinate, Vitamin B5 (Calcium D-Pantothenate), Vitamin B2 (Riboflavin), Potassium Iodide, Vitamin B9 (Folic Acid), Sodium Molybdate, Sodium Selenite], Protein World Thermogenic Blend [Guarana Extract (22% caffeine) & Green Tea Extract (40% polyphenols)], Natural Flavourings, Sucralose (sweetener), Protein World Digestive Enzyme Blend (Lactase 3,500 ALU, Protease 10,000 HUT/g), Protein World Friendly Bacteria Blend (Bifidobacterium Bifidum, Lactobacillus Acidophilus).
Vanilla Ice Cream Nutritional Table
100g 40g serving 40g serving with 400ml almond milk % Adult RI*
Energy (kJ) 1578 631 850
Energy (kcal) 376 150 202
Total Fat (g) 5.3 2.1 6.5
of which saturates (g) 3.2 1.3 1.7
Carbohydrate (g) 21.9 8.8 8.8
of which sugars (g) 19.2 7.7 7.7
Fibre (g) 4.1 1.6 3.2
Protein (g) 58.5 23.4 25.0
Salt (g) 0.89 0.36 0.88
Vitamin A (ųg) 660.0 264.0 264.0 33%
Vitamin D3 (ųg) 4.1 1.7 1.7 33%
Vitamin E (mg) 0.0 0.0 7.2 60%
Vitamin K2 (ųg) 61.9 24.8 24.8 33%
Vitamin C (mg) 66.0 26.4 26.4 33%
Vitamin B1 (Thiamin) (mg) 0.7 0.3 0.4 33%
Vitamin B2 (Riboflavin) (mg) 0.5 0.2 0.5 33%
Vitamin B3 (Niacin) (mg) 9.1 3.6 5.4 33%
Vitamin B6 (Pyridoxine) (mg) 1.1 0.4 0.5 33%
Vitamin B9 (Folic Acid) (ųg) 131.0 52.4 66.0 33%
Vitamin B12 (Cobalamin) (ųg) 0.0 0.0 1.5 61%
Vitamin B7 (Biotin) (ųg) 0.0 0.0 18.0 36%
Vitamin B5 (Pantothenic Acid) (mg) 4.0 1.6 1.8 30%
Potassium (mg) 660.0 264.0 500.0 25%
Sodium (mg) 357.7 143.1 351.1 15%
Calcium (mg) 412.0 164.8 240.0 30%
Phosphorus (mg) 210.0 84.0 238.8 34%
Magnesium (mg) 92.3 36.9 112.5 30%
Iron (mg) 9.6 3.8 4.8 30%
Zinc (mg) 5.9 2.3 3.3 33%
Copper (mg) 0.0 0.0 0.4 36%
Manganese (mg) 0.2 0.1 0.7 33%
Selenium (ųg) 44.3 17.7 18.2 33%
Chromium (ųg) 33.0 13.2 13.2 33%
Molybdenum (ųg) 41.3 16.5 16.5 33%
Iodine (ųg) 122.4 48.9 49.5 33%
Protein World Thermogenic Blend
Guarana Extract (22% caffeine) (mg) 250 100 100 -
Green Tea Extract (40% polyphenols) (mg) 250 100 100 -
Protein World Digestive Enzyme Blend
Lactase (3500 ALU) (mg) 100 40 40 -
Protease (10,000 HUT/g) (mg) 62.5 25 25 -
Protein World Friendly Bacteria Blend
Bifidobacterium Bifidum (CFU) 2.5 Billion Viable Cells 1 Billion Viable Cells 1 Billion Viable Cells -
Lactobacillus Acidophilus (CFU) 2.5 Billion Viable Cells 1 Billion Viable Cells 1 Billion Viable Cells -
Inulin (g) 4.0 1.6 1.6 -
*Reference intake of an average adult (8400kJ / 2000kcal). - Reference intake not established. Contains 24mg of natural caffeine per serving.
Milk Chocolate Nutritional Table
Vitamin B12 (Cobalmin) (ųg) 0.0 0.0 1.5 61%
Strawberry Milkshake Nutritional Table
Vitamin B12 (ųg) 0.0 0.0 1.5 61%
Biotin (Vitamin B7) (ųg) 0.0 0.0 18.0 36%
Pantothenic Acid (Vitamin B5) (mg) 4.0 1.6 1.8 30%
Banana Split Nutritional Table
Salted Caramel Nutritional Table
Inulin (g) 4.00 1.6 1.6 -
Chocolate Mint Nutritional Table
White Chocolate & Raspberry Nutritional Table
Vitamin B5 (Pantothenic Acid ) (mg) 4.0 1.6 1.8 30%
Peanut Butter Nutritional Table
*Reference intake of an average adult (8400kJ / 2000kcal). - Reference intake not established. Contains 24mg of natural caffeine per serving. Contains nuts.
Cookies & Cream Nutritional Table
Fudge Sundae Nutritional Table
Energy (kJ) 1576 630 849 -
Energy (kcal) 375 150 202 -
Total Fat (g) 5.3 2.1 6.5 -
of which saturates (g) 3.2 1.3 1.7 -
Carbohydrate (g) 20.5 8.2 8.2 -
of which sugars (g) 19.4 7.8 7.8 -
Fibre (g) 6.5 2.6 4.2 -
Protein (g) 58.4 23.4 25.0 -
Salt (g) 0.89 0.36 0.88 -
Protein (g)<

redpajama_set_name: RedPajamaCommonCrawl


In the first episode of Unlock the Bars, we take you to Georgetown, Penang to explore the cultural heritage of the Straits Settlement as well as its vibrant gastronomic scene comprising the best of both worlds – from street food to hidden bars that are all the rage in recent years.
Taking inspiration from the Toh Soon Café, a rustic coffee shop tucked in an alley along Lebuh Campbell, head bartender of Manchu Bar, Penang, Sheng Sjeng recreates the flavours of the classic eggs and toast into a new cocktail.
Bread is toasted inside a charcoal furnace for extra oomph.
Toh Soon Cafe is a popular breakfast spot for local Penangites.
Would you ever think of having a drink that tastes exactly like roti bakar? All the familiar tastes — toasty bread, sweetness from the kaya (coconut and egg curd) and the richness of butter — can be enjoyed in one sip. Manchu Bar's Sheng creates just that – The Morning After as the name suggests pays tribute to the local breakfasts enjoyed by generations. The drink gets its sweet dark caramel notes and a hint of burnt butter from Martell Noblige that is paired with the fragrance of coconut and pineapple for a sweet base. When shaken with fresh egg whites, the concoction is creamy and smooth, perfumed by the aroma of torched coconut to infuse a touch of smokiness to the drink.
Sheng demonstrating the making of 'The Morning After'.
Want to try it at home? Sheng shares the recipe of this inventive cocktail.
The Morning After made with Martell Noblige.
Otherwise, make a trip up north to Penang to have a go at this roti bakar-inspired cocktail for a limited time only. The Morning After will be available in Manchu Bar, Penang from 1st to 31st December 2018.


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Now displaying: April, 2020
S4E2 - They're Just in Russia 0
That awkward moment when you think you've traveled to a mystical land but actually you just went to Russia... so relatable. In the strangest and most controversial episode to date, Xena proves to have no real agency or autonomy in her own life. Despite telling a very interesting story about Xena's past (back in season 3), the show seems hell-bent on retconning all of it so they can shove Alti into the storyline. Chris says it best: "I don't know why much of this is happening... which I think is probably a flaw."
We talk about Xena Warrior Princess, Season 4 Episode 1: Adventures in the Sin Trade, part 2
S4E1 - A Classic Skoodlee-doo 0
We're sorry, Xena, but your Gabby is in another underworld! Season 4 starts off with a weird bang, setting the tone of cultural appropriation that will wreak havoc on the rest of the season. Xena skitches into Amazon heaven to find her dead friend, and instead uncovers the evil plot of a dark figure from her dark past. Aka, tldr, not only was Xena a bloodthirsty warlord hell-bent on world domination; she was also corrupted by an evil witch!


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Biografia
Nel 2005 diventa campione italiano cadetti sui 1000 m.

Due piazzamenti in finale ai campionati italiani allievi del 2006: sesto sui 400 m indoor e decimo negli 800 m all'aperto.

Nel 2007 partecipa ai campionati italiani allievi, gareggiando entrambe le volte sui 400 m, aggiudicandosi l'argento agli indoor ed il bronzo all'aperto.

Dopo il bronzo sui 400 m agli italiani juniores del 2008, nel 2009 diventa campione italiano juniores indoor sugli 800 m insieme all'argento sui 400 m (quest'ultima medaglia doppiata anche all'aperto, in cui non parte nella finale degli 800 m).

Sempre nel 2009 vince la medaglia di bronzo con la staffetta 4x400 m agli Europei juniores di Novi Sad in Serbia, dopo essere uscito in semifinale sui 400 m.

Nel 2010 diventa campione italiano promesse sui 400 m, dopo aver vinto il bronzo sulla stessa distanza ai nazionali promesse indoor (decimo sugli 800 m).
Partecipa ai campionati italiani assoluti sia indoor che outdoor, giungendo tutte e due le volte quinto sui 400 m.

Titolo di campione italiano promesse sui 400 m nel 2011 e sesto classificato agli assoluti di Torino.

Prende parte agli Europei under 23 di Ostrava in Repubblica Ceca: esce in batteria sui 400 m e termina ottavo con la staffetta 4x400 m.

Nel 2012 salta l'intera stagione agonistica dei campionati italiani assoluti e di categoria,

Nel biennio 2013-2014 entrambe le volte arriva quinto sui 400 m agli assoluti indoor e non supera la batteria all'aperto.

Doppia medaglia ai campionati italiani assoluti del 2015: argento sui 400 m indoor e bronzo all'aperto (fuori in batteria nei 200 m).

Esordisce con la Nazionale seniores in occasione dell'Europeo per nazioni a Čeboksary in Russia, concludendo all'ottavo posto con la staffetta 4x400 m.

Il 6 marzo del 2016 conclude al quinto posto la finale dei 400 m agli assoluti al coperto.

Progressione

400 metri piani

400 metri piani indoor

Palmares

Campionati nazionali
 2 volte campione promesse nei 400 m (2010, 2011)
 1 volta campione promesse indoor negli 800 m (2009)
 1 volta campione cadetti nei 1000 m (2005)

2005
  ai Campionati italiani cadetti e cadette, (Bisceglie), 1000 m - 2'37"68

2006
 6º ai Campionati italiani allievi-juniores-promesse indoor, (Ancona, 400 m - 52"61
 10º ai Campionati italiani allievi e allieve, (Fano), 800 m - 1'58"72

2007
  ai Campionati italiani allievi-juniores-promesse indoor, (Genova, 400 m - 50"29
  ai Campionati italiani allievi e allieve, (Cesenatico), 400 m - 50"65

2008
  ai Campionati italiani juniores e promesse, (Torino), 400 m - 48"74

2009
  ai Campionati italiani allievi-juniores-promesse indoor, (Ancona), 400 m - 48"91
  ai Campionati italiani allievi-juniores-promesse indoor, (Ancona, 800 m - 1'55"90
  ai Campionati italiani juniores e promesse, (Rieti), 400 m - 47"90
 In finale ai Campionati italiani juniores e promesse, (Rieti), 800 m -

2010
  ai Campionati italiani allievi-juniores-promesse indoor, (Ancona, 400 m - 49"23
 10º ai Campionati italiani allievi-juniores-promesse indoor, (Ancona, 800 m - 1'57"65
 5º ai Campionati italiani assoluti indoor, (Ancona, 400 m - 49"32
  ai Campionati italiani juniores e promesse, (Pescara), 400 m - 47"83
 5º ai Campionati italiani assoluti, (Grosseto), 400 m - 47"32

2011
  ai Campionati italiani juniores e promesse, (Bressanone), 400 m - 47"02
 6º ai Campionati italiani assoluti, (Torino), 400 m - 46"94

2013
 5º ai Campionati italiani assoluti indoor, (Ancona), 400 m - 48"06
 In batteria ai Campionati italiani assoluti, (Milano), 400 m - 47"79

2014
 5º ai Campionati italiani assoluti indoor, (Ancona), 400 m - 48"08
 In batteria ai Campionati italiani assoluti, (Rovereto), 400 m - 48"20

2015
  ai Campionati italiani assoluti indoor, (Padova), 400 m - 47"39
  ai Campionati italiani assoluti, (Torino), 400 m - 46"75
 In batteria ai Campionati italiani assoluti, (Torino), 200 m - 22"04

2016
 5º ai Campionati italiani assoluti indoor, (Ancona), 400 m - 48"49

Altre competizioni internazionali
2008
  nella Coppa del Mediterraneo ovest juniores, ( Rabat), 4x400 m - 3'13"08

2009
 5º nell'Incontro internazionale juniores indoor Francia-Germania-Italia, ( Metz), 800 m - 1'56"00
  nell'Incontro internazionale del Mediterraneo juniores, ( Madrid), 3'12"94

2015
 8º all'Europeo per nazioni, ( Čeboksary), 4x400 m - 3'06"71

Note

Collegamenti esterni

  Francesco Cappellin  su all-athletics.com
 Francesco Cappellin su atletica.me

redpajama_set_name: RedPajamaWikipedia


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News January 21, 2014 <March 13, 2018
Sophomores win Mana Cup
by Kyle Kadomoto
Sophomores dominated Spirit Week 2014 in three of four major categories: All-School Cheer, Mascot, and Pepper Squad, giving them the victory and the Mana Cup.
They finished with a total of 305 points, while the seniors finished with 278 points, and the juniors came in third with 266 points. It was an unexpected win, especially from the perspective of upperclassmen. Junior class president Jaryd Sugihara said, "I don't think anyone expected the sophomores to come back as strongly as they did."
"I don't think anyone expected the sophomores to come back as strongly as they did."
In the first week back from Winter Break, students began the third quarter of school showing off their athletic workout clothes for Spirit Week. The week allows students to show off their creativity through themed dress up days and competitiveness through student council-led activities each day. This year's theme was Misunderstood Monsters. "Our inspiration [for] Misunderstood Monsters," Student Council President Mark Shiroma said, "was a twist on how we always have themes of obvious heroes…cause maybe sometimes we just have to walk in the shoes of other people." Every grade chose a specific monster to represent them for the week.
The week started off with the familiar Tug-of-War challenge, with seventh grade battling eighth grade, freshmen versus sophomores, and juniors versus seniors. The sophomores led at the end of this day as both the girls and boys won their Tug-of-War events.
On the second day, each grade dressed up according to a specific era. The main event on this day was inspired by the television show, Minute-To-Win-It (MTWI). In MTWI, students had to complete assigned tasks in a minute or less. These tasks ranged from keeping three balloons afloat to removing all the tissues from a Kleenex box one at a time. In previous years, Day Two would feature a relay race instead, with the fastest team receiving the most points. Student Council Tony Traughber explained the change, saying, "The relay had unintentional inconsistencies. [We wanted to] include a good [number] of students and [have] more clarity and decision."
Sophomore Isabel Wiemken liked the change. "With Minute to Win It," she said, "everyone was doing the same thing at once and it was so much more organized." By the end of Tuesday, the competition was stiff, as the sophomores and seniors were separated by only five points.
Wednesday's dress up theme was Hip-Hop/Diva. Traditionally, there are no activities planned for Day Three, but to keep the momentum continuous, the student council hosted the first-ever eating contest at Spirit Week. Student council president Mark Shiroma described it as a way "to keep the grades pumped." One student per grade was selected to eat a peanut butter & jelly sandwich, a banana, crackers, Gushers, and drink Kool-Aid and water in the fastest time possible. Senior Micah Ito came in first for the seniors, giving his class a one-point lead in overall points over the sophomores by the end of the day.
Thursday's assembly showcased each grade's Pepper Squad, which had to be performed in seven minutes and include the class cheer. Each skit showcased the backstory to the grade's assigned monster. "This year's Pepper Squad was different," said Traughber. "They were much more entertaining and incorporated more dancing."
"[This year's pepper squads] were much more entertaining and incorporated more dancing."
Friday marked the end of the competitive week and students came dressed in HBA school colors—black, white, or gold. The assembly program was jam-packed with two big activities, and introducing the boys and girls basketball team, and the cheerleading team. The program began with Platform Jam, where classes had to fit as many people on a platform as possible and stay on for 30 seconds. After that, each grade performed their version of the All-School Cheer, which was their last opportunity to earn points for the week.
To cap off the day, the girls and boys varsity basketball teams won their respective homecoming games. The girls led the way their entire game, dominating La Pietra 39-7. The boys played Damien in a close, action-packed game. Trailing the entire game, they closed the gap in the fourth quarter, outscoring Damien 18-6. They then pulled out a narrow win in front of a capacity crowd—about 1,000 people—winning 51-48.
As with previous Spirit Weeks, the National Honor Society organized a fundraiser to benefit a non-profit organization. This year, more than $5,000 was collected for ZOE International, an agency that works to save women and children from human trafficking. Grades were also awarded Spirit Week points for their donations.
Kyle Kadomoto
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Summer Freedoms


Liquid propane dispensers were tested on the Wasatch Plaleau of central Utah during the winters of 1992-93 and 1993-94. Remote operation of the radio-controlled dispensers proved to be highly reliable, in large part because of the mechanical simplicity of the devices. A prototype fully-automated liquid propane seeding system was tested during early 1994 on the west (windward) slopes of the Plateau. An icing rate device was used to detect supercooled liquid water at the center station of three exposed propane dispenser stations. Wimd speed and direclion and air temperature were also monitored at the center station. When certain predetermined weather criteria were met, the three propane dispensers were automatically turned on. Propane continued to be dispersed until one or more of the weather criteria were out-of-bounds for 2 h. Post-season analysis of recorded data showed that the fully-automated seeding system operated as designed for the most part. Some minor problems were encountered, but can easily be corrected. Recommendations are made for simplifying the automated decision process. A means of detecting supercooled liquid water in the absence of commercial electrical power was also tested during early 1994. It proved to be practical and reliable. Accordingly, the technology exists to operate fully-automated networks of liquid propane dispensers in remote mountain locations. Costs of such networks would not be excessive, and reliability can be expected to be high. However, further physical experimentation is recommended to document the effectiveness of liquid propane seeding for snowfall enhancement because such evidence is quite limited.


I just wanted to share this cute pic I took of Monster and myself while we waited for the bus yesterday morning in the rain. It's hard to get non-posed pictures when you're taking them yourself, but I always snap a few and I love how this came out.
love your hair! i never got blue hair to look right on me. do you have a secret for it?


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Matt Gaetz Reveals Republicans Will Release the 14,000 Hours of J6 Tapes That Have Been Hidden
Rep Matt Gaetz (R-FL) announced to Charlie Kirk on Tuesday, on the Charlie Kirk show, that the Republican-led House would release the long-hidden trove of video footage from Jan. 6th. 2021 which has been at the center of the disagreement between the political prisoners- and the government.
"Kevin McCarthy told us he's going to get the evidence out in front of the American people, and that means releasing the 14,000 hours of tapes that have been hidden that I think would give more full context to that day, rather than the cherry-picked moments that the January 6th committee tried to use to inflame and further divide our country," Gaetz told Kirk.
The government has denied almost everyone access to their footage, admitting that if people see that dangerous footage, it will destroy the opportunity they have to build a false political narrative about what happened that day.
David Sumrall, the founder of StoHate.com, who is one of two official investigators for some of the J6 defendants, was allowed to see the government's footage. The agreement Sumrall had was that he could describe the footage to the media but not show anyone any of the footage.
Sumrall has investigated tens of thousands of hours of footage and compiled an expert amount of information on the events that unfolded on the 6th.
He recently posted about the deeds of the Capitol Police from that day:
Here's a picture of the @CapitolPolice who shot Joshua Matthew Black in the cheek with a rubber bullet FOR NO REASON at 1:07 PM on #January6 right after a man on a megaphone implored "Please do not fire on us! We don't mean you any harm." #PoliceBrutalityhttps://t.co/ZuylwocLQN pic.twitter.com/HFRfSLKRtF
— StopHate.com🛑 (@HelpStopHate) January 10, 2023
Sumrall has said that when the American people see the footage, they are going to be shocked and dismayed at what they see- pointing primarily to the death of one woman who was screaming for help, as the police continued to attack the rally participants.
He recently posted about one of the victims who was shot that day:
Here's a familiar face.
I don't really care to research his name. I have seen where he was in the House Chambers with the Qanon Shaman.
I have no ties with these people. I did meet Jacob while on top of the media scaffolding in later documents. pic.twitter.com/joT1EY00Pm
— Tyler Ethridge 🗽 (@realTE13) January 7, 2023
About the footage that is tome come, Sumrall prepares people that what they will see is horrifying.
"You watch her die, right there while the Trump supporters are begging the police to stop and they are trying to help her," Sumrall has said.
There is no doubt that the footage is bombastic and graphic and will expose the government's role in creating a disaster.
The government has denied the J6 defendants access to the film as well, in a highly unethical and unconstitutional move.
Hope among voters was that if Republicans took over the US House, as they have, the Republicans would take the opportunity to destroy the left's highly politicized messaging about what happened that day, and in doing that- the political prisoners who have been held without trials- would begin to see some relief.
The release of the footage, which Gaetz is saying is imminent, is likely to show that the Democrats and the RINO proponents of the unconstitutional US House committee were lying about everything.
When that day comes, it will show that the media was in conclusion with the Democrats to frame Americans and unfairly harass supporters of President Donald J. Trump.
Also, the number of deaths, including the shooting death of Ashi Babbitt by a Capitol Police officer, could be examined in a new way.
There is hope that in that film lies some answers about the roles that the different law enforcement agencies on the ground that day played.
There is speculation among many of the participants that it was the Capitol Police themselves who created the violent uprising that caused up to 5 deaths.
"Gaetz told Charlie Kirk that releasing the tapes "would give full context to that day rather than the cherry-picked moments of the January 6th committee." He further explained this is one example of the deal made with Kevin McCarthy to provide the American people the truth rather than the lies propagated by the Biden Regime," Cullen Linberger reported for the Gateway Pundit.
"Looks like truth about Pelosi's Fed-Op is going to be exposed even further,"
DC_Draino (@DC_Draino) posted on Twitter on January 10, 2023.
Gaetz: Republicans will release "14,000 hours of [J6] tapes that have been hidden"
Looks like truth about Pelosi's Fed-Op is going to be exposed even further!
pic.twitter.com/ybf3RSgoow
— DC_Draino (@DC_Draino) January 10, 2023
Here is the relevant part of their conversation, GWP reported:
Kirk: One of my favorite members of Congress is Thomas Massie and the fact that he is going to be on this new committee (the Church Committee) really gives me hope.
Matt, do you anticipate allowing the dogs to be released if you will against this fourth branch of government (the Deep State).
Gaetz: Kevin McCarthy told us he is going to get the evidence out in front of the American people and that means releasing the 14,000 hours of tapes that have been hidden that I think would give more full context to that day (January 6th) rather than the cherry-picked moments the January 6th committee tried to use to inflame and further divide out country.
So yes, I do believe that part of this deal is a concession that we are going to get the truth out in front of the American people.
Should Congress actually follow through and release the tapes, this will serve as the first step toward getting justice for the January 6th prisoners. The Gateway Pundit has provided numerous accounts of the horrifying conditions the Regime has placed them in.
BREAKING: Matt Gaetz says we can finally expect the release of 14,000 Hours of January 6 footage that has been withheld by Democrats.
This day just gets better and better!🎊🔥🇺🇸
— James Bradley (@JamesBradleyCA) January 10, 2023


text:
\section[Introduction]{Introduction}
	Rapid development in imaging technologies has made medical imaging one of the most important techniques in providing essential evidence for clinical diagnosis and medical intervention. We can now collect unprecedented amounts of image data in both spatial and temporal dimensions per patient, from one or more modalities. The challenges in extracting valuable information from these images motivate modern updates and developments in image processing and analysis methods and tools. For example,  three recent imaging books by Russ and Neal\cite{JRuss2016}, Birkfellner\cite{WBirkfellner2014}, and Burger and Burge\cite{WBurger2016} show the fundamentals of image processing methods developed in the last score. Industries have also invested in developing image processing tools to accommodate many different modalities with types of images ranging from document imaging \cite{docimage} and multimedia imaging \cite{videopross} to medical imaging \cite{leadtools}. The National Institute of Health (NIH)'s Center for Information Technology is leading the {\it Medical Image Processing, Analysis, and Visualization} (MIPAV) project for quantitative analysis of medical images \cite{nihmipav}. Most of the functionalities in MIPAV are for image (pre)processing of  PET, MRI, CT, microscopy, 3D images and other widely-used images in medicine. One of the most widely used image analysis that MIPIV provides, uses summary statistics of the volume of interest (VOI) within a single image to generate contour VOI plots for all of the images to be analyzed.

		Comparing images or assessing their similarities is one of the most important objectives in image analysis. The methods for such comparisons include the methods for pixel by pixel comparison and those based on a specific image measure \cite{Katukam2015}. The measure-based methods include Least Square Image Matching (LSM) \cite{Forstner1982}, Hausdorff distance  \cite{Huttenlochter1993} and components of  a matrix decomposition \cite{Hocenski2000}. Among these measures, LSM has been used extensively because of its simplicity, but it may suffer from inaccurate initial input value and outliers \cite{Forstner1982}.  To obtain comprehensive details of the differences over entire imaging region,  a statistical pixel by pixel comparison is needed.

		Denote an  image sequence as spatial and temporal data  $\boldsymbol{Y}(s,t,n)$, where $\boldsymbol{Y}$ is the intensity value at the pixel or spatial location $\boldsymbol{s} \in \boldsymbol{S}$ and time $\boldsymbol{t} \in \boldsymbol{T}$  for the subject $\boldsymbol{n} \in \boldsymbol{N}$. Here  $\#\boldsymbol{S}$, $\#\boldsymbol{T}$, $\#\boldsymbol{N}$ are the sizes of sets $\boldsymbol{S}$, $\boldsymbol{T}$, and $\boldsymbol{N}$ respectively. Then, the number of measurements  per subject, or the data dimension is $\boldsymbol{p} = \#\boldsymbol{S} \times \#\boldsymbol{T}$. This $\boldsymbol{p}$ can be huge, leading to a large-p data problem.  If  $\#\boldsymbol{N}$ is much smaller than $p$, we have a large-p-small-n problem. Large-p data challenges statistical multiple comparisons, no matter the number of subjects $\#\boldsymbol{N}$ is large or small. How to obtain an objective threshold for multiple comparisons of large, noisy and potentially heterogeneous images has challenged statisticians, especially for analyzing overflowing simultaneous images. Statistical parametric mapping (SPM) \cite{Worsley2002} is an effective and widely-used pixel by pixel image comparison approach.   The motivation for developing  \textbf{L}ongitudinal \textbf{I}mage \textbf{S}equence \textbf{A}nalysis (\pkg{LISA})  MATLAB package comes from generalizing and making a one-stop ensemble of image processing and analysis tool for complex, longitudinal images,  by transforming the components of the Longitudinal Analysis and Self-Registration (LASR) procedure \cite{XWang2006}.  The LASR was developed to overcome the heterogeneity, and the large-p-small-n issue in assessing the effect of Neuromuscular Electrical Stimulation (NMES) experiments \cite{NBergstrom2004} in reducing the risk of developing pressure ulcers.  The data for LASR were movies composed from sequences of seated pressure mat images obtained under a static and a dynamic stimulation, respectively,  in each session from each visit by a patient, for eight patients observed up to 5 years. There were artifacts, heterogeneity, noises, and substantial spatial and temporal alignment issues from different sessions even on the same patient. The data was massive with a huge $p$ and a small $n$ of 8.   These data required extensive imaging preprocessing and general statistical analysis method, Statistical Non-parametric Mapping (SNM) that is more general than the SPM, for each set of comparisons.   Various code for the components of LASR was developed individually and specifically to the pressure data formate. It would have been easier if the \pkg{LISA},  a general purpose MATLAB package was available then.
With increasing amounts of imaging data from different modalities in nowadays, it becomes imperative to implement and generalize the LASR method into a stand-alone MATLAB package for general application of images in standard image formate.  Making \pkg{LISA} available to public as a one-stop MATLAB package sets a basis for future development  for even large data and more generalized case. See the discussion in Section 6.

\begin{figure}[htb]
		\centering
		\includegraphics[width=\textwidth]{structure.png}
		\caption{Structure of \pkg{LISA}}
		\label{fig:structure}
	\end{figure}

         Figure \ref{fig:structure} above shows the schematic  structure of \pkg{LISA} tool, where the `image processing' is called `image pre-processing,' and `statistical analysis' is called `image post-processing.' The top part of Figure \ref{fig:structure} presents the overall flow of \pkg{LISA}. A complete process of \pkg{LISA} goes through 2 steps: ``I. Image Pre-processing'' step as introduced in the left bottom flowchart of Figure \ref{fig:structure} and ``II. Image Post-processing (Statistical Analysis)'' step as introduced in right bottom flowchart of Figure \ref{fig:structure}. However, if image pre-processing is unnecessary or a user chooses to use other \proglang{MATLAB} built-in tools to de-noise, segment or register their two sequences of images, \pkg{LISA} has the option to skip the image pre-processing step to go directly to image post-processing (statistical analysis). Image pre-processing is done by 1) selecting a region of interest (ROI), 2) de-noising the images by automatic or manual image segmentation, and 3) aligning the images by also automatic or manual image registration. Image post-processing is done automatically by 1) calculating curated differences, 2) de-noising the differences obtained from Step 1 by a non-parametric smoothing, 3) deriving variance-adjusted, smoothed values to get T-type statistics, and 4) providing final significances of the differences based on the multiplicity adjusted p-values. These four post-processing  steps provide Statistical Nonparametric Mapping (SNM) with minimal assumptions on the images. Here we use the False Discovery Rate (FDR) to adjust the multiplicity.

 The development of \proglang{MATLAB} package \pkg{LISA}(Longitudinal Image Sequence Analysis) has implemented and  expanded the functionality of LASR to general cases for any images that can be aligned using a reference line (estimable from the image data directly)  or general image processing tools in Matlab. A graphical user interface in \pkg{LISA}  provides convenience for a user to quickly remove noise and artifacts, decide on the region of interests and choose a quick reference line if feasible. In addition, \pkg{LISA} provides a parallel computing option for a large sequence or high-resolution images.

	In the remaining paper, we provide the \pkg{LISA} installation guide  (\S2),   options for pre-processing (\S3) and  post-processing  (statistical analysis)  with  a brief introduction of methodologies (\S4),  demonstrate the  software \pkg{LISA}  via an application (\S5), and then conclude the article with a discussion, including limitations and further extensions (\S6).   For the details and interpretations of  of  the D, S, T and P maps, see Section 4.  With features introduced in  \pkg{LISA}, we improve the accuracy of statistical comparison for both static and dynamic sequences of images. \pkg{LISA} can tackle two sequences of time-series images in general, including seated pressure mat images used in the NMES study, time-series satellite image\cite{JVerbesselt2010}, and time-series chemical imaging data \cite{Gowen2011}.

	\section[Installation]{Installation}
	A step-by-step installation guide is provided in this section.\\

	\textbf{1. Download \pkg{LISA} App} \\

	\pkg{LISA} program ``LISA.zip'' can be downloaded from the following two locations.

	\begin{description}
		\item[$\cdot$] MathWorks File Exchange website : \url{http://www.mathworks.com/matlabcentral/fileexchange/?term=typ
		\item[$\cdot$] Center for Statistical Research, Computing, and Collaboration(SR2c) website : \url{http://sr2c.case.edu/software.html}
	\end{description}

	The archive ``LISA.zip'' contains three files : LISAxxx.mlappinstall, LISAxxx.prj, and ReadMe where xxx is the package version number. ``LISAxxx.mlappinstall'' file is the installation file, ``LISAxxx.prj"' is a project file containing package information and ``ReadMe'' file is a quick starting guide.\\

	\textbf{2. Install and load \pkg{LISA} App} \\

	With a newer version of \proglang{MATLAB} (version 8 or above), one can install and load \pkg{LISA} by simply running the LISAxxx.mlappinstall file \cite{installpackage} and follow the three steps below. However, with an older version of \proglang{MATLAB} (version 7 or below) one can manually install \pkg{LISA} using the  instructions provided in the following MathWorks website \url{https://www.mathworks.com/help/matlab/matlab_env/manage-your-add-ons.html}.

	\begin{enumerate}
		\item Run LISAxxx.mlappinstall file. Then \proglang{MATLAB} will be launched and a ``Install'' window will pop-up as shown in Figure \ref{fig:install} (a)
		\item Continue by clicking the ``Install'' button. \proglang{MATLAB} will automatically install the package and will notify the installer from the ``APP'' tab when the installation is complete, as shown in Figure \ref{fig:install} (b)
		\item Load \pkg{LISA} by clicking on the \pkg{LISA} App icon under ``APPS - MY APPS'' as shown in Figure \ref{fig:package}
	\end{enumerate}

	\begin{figure}[H]
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[][Installation Window]{\includegraphics[width=4.5cm]{install1.png}\label{<figure1>}}
	\end{minipage}
	\begin{minipage}{.5\linewidth}
		\subfloat[][Installation Complete]{\includegraphics[width=4cm]{install2.png}\label{<figure2>}}
	\end{minipage}
		\caption{Installation Process}
		\label{fig:install}
	\end{figure}

	\begin{figure}[H]
		\centering
		\includegraphics[width=10cm]{install3.png}
		\caption{Where \pkg{LISA} can be found and launched}
		\label{fig:package}
	\end{figure}

	When \pkg{LISA} is loaded, a user can start using the program right away by running the \code{lisa} function. Typing ``helpLISA'' into \proglang{MATLAB} command window will provide a vignette including the syntax and examples of the \code{lisa} function.

\section[Dataprocessing]{Data and Image Processing}
\pkg{LISA} is bulit to compare two sequences of images by contrasting each sequence with representing images taken in one session. The first stable image of each sequence is treated as its reference image, which will be used throughout the image processing. Data and image processing consists of the following 5 steps.
\begin{enumerate}[nolistsep]
	\item Data Scan : Scan image frames from ``csv" files
	\item Region of Interest(ROI) Selection : Crop images to the region of interest
	\item Image Segmentation : Detect noise
	\item Image Registration : Calibrate Images
	\item Finer ROI Selection(optional)
\end{enumerate}

\subsection{Data Scanning}
The current version of \pkg{LISA} accepts ``csv" files as inputs. A user can convert various image formats to a ``csv'' format in \proglang{MATLAB} by reading images using \code{imread} and saving the image to a ``csv'' file using \code{csvwrite}. In general, the output of an imaging tool in a ``csv'' file includes information on the date, time, frame numbers, intensity values of the images and metadata such as equipment model, user information etc. Therefore, it is very important to distinguish the image intensity matrix in a numerical form to conduct statistical comparison. \pkg{LISA} is able to extract image intensity matrices from the raw data files that are in the following three formats as shown in figures \ref{fig:scandata}(a), \ref{fig:scandata}(b), and \ref{fig:scandata}(c).

\begin{figure}[H]
	\begin{minipage}{.325\linewidth}
		\centering
		\subfloat[Row Identifier]{\includegraphics[width=5cm]{rowscan.png}\label{<figure1>}}
	\end{minipage}
	\begin{minipage}{.325\linewidth}
		\centering
		\subfloat[Column Identifier]{\includegraphics[width=5cm]{colscan.png}\label{<figure2>}}
	\end{minipage}
	\begin{minipage}{.325\linewidth}
		\centering
		\subfloat[Blank Row]{\includegraphics[width=5cm]{blankscan.png}\label{<figure3>}}
	\end{minipage}
	\caption{Data Matrix Scanning Method}
	\label{fig:scandata}
\end{figure}

The data structure in Figure \ref{fig:scandata}(a) requires a user to extract the data matrix using a row identifier. A user needs to set the parameter \code{`scantype'} to \code{`row'}  and a row identifier character string needs to be passed over to the parameter \code{`rowid'}. \pkg{LISA} skips the header rows until it encounters the first identifier. It then scans next \texttt{`nrow'} number of rows to extract as a numeric matrix and define it as the first image frame. \pkg{LISA} continues the same steps until it reaches the last identifier. Here is an example of the syntax for scanning the data using the row identifier having all other arguments with their default values.\\

\code{lisa(`file1.csv',`file2.csv',`row',\#frame,\#row,\#col,`rowidentifier',\\`',`',`',`',`',`',`',`' )}\\

The data structure in Figure \ref{fig:scandata}(b) requires a user to extract the data matrix using a column identifier. A user needs to specify the column that contains information on the frame numbers by setting the parameter \code{`scantype'} to \code{`col'}  and a column number needs to be passed over to the parameter \code{`colid'}. \pkg{LISA} reads the number in that column to extract numeric matrix and save each matrix to its corresponding frames. Here is an example of the syntax for scanning the data using the column identifier having all other arguments with their default values.\\

\code{lisa(`file1.csv',`file2.csv',`col',\#frame,\#row,\#col,` ',\# of column\\identifier,`',`',`',`',`',`',`',`' )}\\

The data structure in Figure \ref{fig:scandata}(c) requires a user to extract the data matrix using a blank row right before the numeric intensity matrix. A user needs to set the parameter \code{`scantype'} to \code{`blank'} and \pkg{LISA} skips the header rows until it encounters the first blank row. It then scans next \code{`nrow'} number of rows to extract as a numeric matrix and define it as the first image frame. \pkg{LISA} continues the same steps until it reaches the last blank row. Here is an example of the syntax for scanning the data using a blank row having all other arguments with their default values.\\

\code{lisa(`file1.csv',`file2.csv',`blank',\#frame,\#row,\#col,`',`',`',`',\\`',`',`',`',`' )}

\subsection{Select Region of Interest}
This is the first interactive step for a user. A user is given the opportunity to select a rectangular region of interest from the first images of each sequence. Cropping images to a specific area of interest enhances the result of data segmentation and data registration because this naturally eliminates some part of the noise. \pkg{LISA} uses the ``\code{imcrop}" function within the \proglang{MATLAB Image Processing Toolbox} to select the region of interest.

\subsection{Image Segmentation}
 It is critical to reduce noises and outliers by segmenting an image, which  sets the noisy intensity values to zero before performing an image registration. LISA provides data-driven automatic and manual segmentation. A  pixel is identified as noise if its intensity value is less than a threshold $C$. Let $Y (i, j)$ denote the pixel intensity value at the $i$th row and $j$th column of a representative image. Then the intensity values in the segmented image are  $\widetilde{Y}(i,j)$ below:
\begin{equation}
	\tilde{Y}(i,j) = \begin{cases}
	Y(i,j) &\textrm{if $Y(i,j)$ > C}\\
	0 &\textrm{otherwise}
	\end{cases}.
\end{equation}
		The  threshold value  $C$  is derived differently for automatic segmentation and manual segmentation, as shown below.

\subsubsection{Automatic Segmentation}
Automatic segmentation finds an optimal $C$ that separates data into two groups with the maximum separation, based on an estimated mixture of Gaussian distributions of  the pixel intensity values, as introduced in \cite{XWang2006}. The mixture density $f(y)$ has the following form:
\begin{equation}
	f(y) = \displaystyle\sum_{g=1}^{G} \frac{\alpha_g}{\sigma_g}\phi\left(\frac{y - \mu_g}{\sigma_g}\right)\equiv\alpha_1\cdot f_1(y)+ (1-\alpha_1) \cdot f_2(y)
\end{equation}
			where $\phi$ is the standard normal density, $G$ is the number of mixtures,  and $\{\alpha_g > 0, \;g=1, ..., G\}$  are mixing parameters satisfying  $\sum_{g=1}^{G} \alpha_g = 1$. The first-component density $f_1(y) = \phi\left[(y - \mu_1)/\sigma_1\right]/\sigma_1$ represents the background distribution, while  the second-component density $f_2(y) = \sum_{g=2}^{G}(\alpha_g/\sigma_g)\cdot \phi\left[(y - \mu_g)/\sigma_g\right]/(1-\alpha_1)$ represents the distribution of pixel intensity inside the region of interest, also as a mixture of Gaussian distributions. The $C$ value automatically computed by LISA separates $f_1$ and $f_2$ at the valley bottom  between the two densities -  see the black vertical line in Figure 5.

\begin{figure}[H]
	\centering
	\includegraphics[width=9cm]{autoseg.png}
	\caption{Data Segmentation from Histogram and Density Plot}
	\label{fig:segexampleplot}
\end{figure}

Figure \ref{fig:segexampleplot} is an example of the histogram and density plot for the pixel intensity values in a seated pressure map. The distribution of the intensity values is modeled by a mixture of 3-component Gaussian distributions. The threshold $C$ is determined by the vertical line that separates the first and second components in  the fitted parametric Gaussian mixture model.

\subsubsection{Manual Segmentation}
Manual segmentation allows a user to choose  $C$ visually based on a histogram of pixel intensity values. Figure \ref{fig:manuseg} is a histogram of intensity values.  It is natural to select the threshold $C$ at the gap where the target cross is located because at that threshold value the intensity values are distinctively divided into two groups. Once the threshold $C$ is selected, intensity values below $C$ will be replaced with zero, defined as the background noise.

\begin{figure}[H]
	\centering
	\includegraphics[width=9cm]{manuseg.png}
	\caption{Manual Segmentation from Histogram}
	\label{fig:manuseg}
\end{figure}

\subsection{Image Registration}
There are two steps in image registration: temporal registration and spatial registration. Temporal registration excludes unstable images from each sequence and identifies the most similar subset of images to compare between the two sequences. Spatial image registration is done using a geometric transformation of the images.

\subsubsection{Temporal Registration}
The  correlation coefficient of the intensities between two images is  a reasonable measure of similarity between the two images. \pkg{LISA} temporally aligns two image sequences (of same speed) by shifting one sequence  forward $j$
frames to match temporally the conditions of another sequence, using an Intensity-based Correlation Registration (\textbf{ICR})  below.
\begin{enumerate}
	\item Exclude $5$ or the first 5$\%$ of images from each sequence, whichever the number is larger. This step  removes  noise images  at the start of recording.  Let $S^1_1, ..., S^1_n$ and $S^2_1,...,S^2_n$ be the emaining  $n$ sequential images in  two image sequences $S^1$ and $S^2$.
	\item Compute the shift-$j$ correlation coefficient of two sequences by averaging correlation coefficients of  the  data from the  respective $i$-th and $(i+j)$th frames of two sequences over $i = 1,...,n-j$, for each $j = 0,...,n-1$  to obtain:
		$$AvgCor_j = \dfrac{1}{n-j} \sum_{i} cor(S^1_i,S^2_{i+j})$$
	Then find $j_{max}$ that maximizes the correlations coefficients $vgCor_j$ over $j$. In other words  $j_{max}= \min\{j_0:  AvgCor_{j_0} = \max_{j}(AvgCor_j)\}$.
	\item Align $S^1_i$ and $S^2_{i+j_{max}}$ for  frames $i = 1,...,n-j_{max}$.
\end{enumerate}

\subsubsection{Spatial Registration}
\pkg{LISA} provides automatic registration and manual registration for the sequences after a temporal registration,

\paragraph{Automatic Registration}
The following midline registration algorithm  from Algorithm 3.1 in \textit{Wang et al.(2006)}\cite{XWang2006} is applicable to registering  images that can be aligned by a rigid transformation using a midline (and a point) as a natural landmark.

\setlength{\leftskip}{0.4cm} \textbf{Automatic Registration Algorithm}:
\begin{enumerate}
	\item Determine, column by column, the mid-points($m_c$) by counting non-zero values in each column of an image using $m_c$ = $\frac{r}{2} + \frac{(u - l)}{2}$, where $r,u,l$ are the number of rows, number of non-zero intensity values from the upper half and lower half of the column respectively for $c = 1,...,\# of columns$.
	\item Fit a regression line through the mid-points and call this line the mid-line.
	\item Conduct a rigid transformation by aligning each mid-line with a horizontal line as shown in Figure \ref{fig:autoregt} using a rotation and location shift.

	\begin{figure}[H]
		\centering
		\includegraphics[width=9cm]{autoregt.png}
		\caption{Transformation in Automatic Registration}
		\label{fig:autoregt}
	\end{figure}

	In other words, we define the transformation matrix \textbf{R} using the slope and intercept estimates of the mid-line from step 2 as
	$$\textbf{R} = \begin{bmatrix} cos\theta&-sin\theta&s_x\\ sin\theta&cos\theta&s_y\\ 0&0&1 \end{bmatrix}$$
	where $tan\theta$ is the slope of the mid-line and $s_x$ and $s_y$ are the shifting parameters. Using $R$ we change $X$ to $X' = RX$ where $X$ contains the information of the coordinate of the pixel defined as $X = [x,y,1]$.
\end{enumerate}

Figure \ref{fig:autoregistplot} shows an example of automatic registration. The image is translated to be aligned with the horizontal center line. The mid-line registration algorithm given above is fast to implement automatically when comparing sequences of many images. For images that require a rescaling or other types of registration, we suggest a user to use other \proglang{MATLAB} built-in image processing tools or manual registration in the next section.

\begin{figure}[H]
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[][Before Registration:Image and midline]{\includegraphics[width=6cm]{beforeautoregist.png}\label{<figure1>}}
	\end{minipage}
	\begin{minipage}{.5\linewidth}
		\subfloat[][After Registration:Image and 	midline]{\includegraphics[width=6cm]{afterautoregist.png}\label{<figure2>}}
	\end{minipage}
	\caption{An example of automatic spatial image registration}
	\label{fig:autoregistplot}
\end{figure}

\paragraph{Manual Registration}
Manual registration aligns the images using a line defined by two user-selected points. Users select two points on the first images from each sequence which makes four point selections in total. The first points selected for each sequence are used as a reference point which is aligned at (5,\# of rows/2) to shift the image to the same starting point. The second points selected is used to form a line which is used to rotate the image to the horizontal middle line of the image. An illustration of the point selection is provided in Figure \ref{fig:manuregistex}.

\begin{figure}[H]
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[][Points selection for the 1\textsuperscript{st} sequence]{\includegraphics[width=6cm]{manuregselect1.png}\label{<figure1>}}
	\end{minipage}
	\begin{minipage}{.5\linewidth}
		\subfloat[][Points selection for the 2\textsuperscript{nd} sequence]{\includegraphics[width=6cm]{manuregselect2.png}\label{<figure2>}}
	\end{minipage}
	\caption{An example of point selection in manual registration}
	\label{fig:manuregistex}
\end{figure}

Then \pkg{LISA} will perform a rigid transformation based on a shift and rotation matrix of \textbf{R} defined by the dotted blue line and 1\textsuperscript{st} points from Figure \ref{fig:manuregistex}, where \textbf{R} = $\begin{bmatrix} cos\theta&-sin\theta&u\\ sin\theta&cos\theta&v\\ 0&0&1 \end{bmatrix}$. The mathematics behind the transformation using the matrix \textbf{R} us identical to step 3 in the automatic registration.

\subsection{Optional ROI selection}
\pkg{LISA} provides an optional region of interest selection after the image registration as shown in Figure \ref{fig:froiresult}. This step is done by connecting a polygon around the ROI to distinguish ROI from the background by setting intensity values outside of the ROI to zero. Figure \ref{fig:froiresult} shows an example of optional ROI selection after registration. Comparing figure \ref{fig:froiresult}(a) and figure \ref{fig:froiresult}(b), regions at y = 7 to 15 at x = 30 of the image in figure \ref{fig:froiresult}(b) is trimmed off after the selection.

\begin{figure}[H]
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[][Before optional ROI Selection]{\includegraphics[width=6cm]{froiresultbefore.png}\label{<figure1>}}
	\end{minipage}
	\begin{minipage}{.5\linewidth}
		\subfloat[][After optional ROI Selection]{\includegraphics[width=6cm]{froiresultafter.png}\label{<figure2>}}
	\end{minipage}
	\caption{Example of optional ROI selection.}
	\label{fig:froiresult}
\end{figure}

This step is optional to be used only when it is necessary because a user needs to be cautious when selecting the ROI at this step. If the ROI is accurately selected, it will improve the accuracy of our statistical analysis. However, if the ROI selection includes artifacts or strong noises by accident, it will corrupt the image and possibly lead to misleading significance regions.

\section[Statanalysis]{Statistical Analysis}
Our primary objective for \pkg{LISA} is to identify the areas where there have been significant changes. The statistical methods to achieve this goal are given in the following four steps, by revising the Algorithm 4.1 in our early work \cite{XWang2006}.

\begin{enumerate}[nolistsep]
	\item {Curated difference}
	\item {Non-parametric smoothing map}
	\item {Generalized T-Map}
	\item {FDR-controlled P-Map}
\end{enumerate}

\subsection{Curated Difference}
The initial step is to calculate curated difference of intensity values between the pairs of images in the two sequences being compared. For an image with a resolution of $n$ by $m$, the image contains $N$ number of pixels where $N$ = $n \times m$. Let $Y_1(i,j,t)$ and $Y_2(i,j,t)$ be the pixel intensity values at coordinate $(i,j)$ of the corresponding images at time $t$ in sequence 1 and sequence 2 respectively. Then the curated difference $d(i,j,t)$ is a pixel by pixel subtraction defined as $d(i,j,t) = Y(i,j,t) - Y(i,j,t)$ for $i = 1, ..., n$, $j = 1, ..., m$ and $\forall{t}$ are calculated for every pair of image comparison from the two image sequences and are used to generate the time series D-maps.

\subsection{Non-parametric smoothing map}
Our non-parametric smoothing map first pads the border of curated difference and then smooth the curated difference, $d_{j}$,  in  the region of interest. Padding the edge will prevent outstanding false-positive differences due to image alignment and noise. Our non-parametric smoothing method uses a local polynomial regression. The method is reproduced from 4.1.T-type Statistics \cite{XWang2006} as follows.

For a single set of curated difference image data $\{ (x_{1j}, x_{2j}, d_j); j=1,...,N\}$, consider  the model
$$d_{j} = m(x_{1j}, x_{2j}) + \epsilon_j$$
where $m(\cdot)$ is an unknown smooth function and $\epsilon_i$ is an error term representing the random errors of the curated differences derived from the image observations. Then the  smooth function $m$ at
$s=(x_1, x_2)$ can be approximated by the value of  a  fitted local polynomial function using the intensity values in the  neighborhood of $s$. Specifically, for any $s=(x_{1}, x_{2})$ of interest, the least squares problem seeks  $\hat{\boldsymbol{\beta} }= (\hat\beta_0, ..., \hat\beta_5)$ such that
\begin{eqnarray*}
\hat{\boldsymbol\beta} &=&\min^{-1}_{\boldsymbol{\beta}} \sum_{j=1}^N \left\{d_j - \beta_0 - \beta_1(x_{j1}-x_1) - \beta_2(x_{j2}-x_2) - \frac{\beta_3}{2}(x_{j1}-x_1)^2 - \frac{\beta_4}{2}(x_{j2}-x_2)^2\right.\\
& &\left.\qquad  \qquad \qquad- \beta_5(x_{j1}-x_1)(x_{j2}-x_2) \right\}^2 w(x_{j1}-x_1; h_1) w(x_{j2}-x_2; h_2)
\end{eqnarray*}
and then defines the estimate of the regression surface as $\hat{m}(x_1,x_2) = \hat{\beta}_0$ \cite{WCleveland1988,XWangNon2010}. The smooth weight function $w(z; h)$ peaks at $z=0$ and the smoothness of $\hat{m}$ is controlled by the bandwidth $h$. A normal density function with mean 0 and standard deviation $h$ is a typical choice. In our procedure, the bandwidths $h_1$ and $h_2$ are selected based on the approach of 10 fold cross validation with 20 iterations \cite{Hastie2001}.

\subsection{Generalized T-Map}
The method used for generalized T-Map is reproduced from 4.1.T-type Statistics \cite{XWang2006} as follows. \\
The hypothesis we are testing is $H_0 : m(s) = 0$ vs. $H_1 : m(s) > 0$ at $s = (x_1, x_2)$. Where $m$ can be written as a linear combination of the actual curated differences $d_j$ of pixel values, $$\hat{m}(s) = \sum_{j=1}^{N}p_j(s) y_j$$ 	where $p(s)^{T} = (p_1(s),...,p_N(s))$ are the rows of the hat matrix specified by the local quadratic approximation. The estimated standard deviation of the local estimate $\hat{m}$ is $\hat{S}(s) = \hat{\sigma}\lVert (s) \rVert$. From these estimates, a T-type statistics is derived as,	$$T(s)  = \frac{\hat{m}(s)}{\hat{S}(s)}$$ $T(s)$ follows t-distribution and the null hypothesis is rejected at $s$ when $T(s) > t_{1-\alpha}(\sigma_1^2/\sigma_2)$ under a significance level of $\alpha$ with the degrees of freedom $n-6$, and $\sigma_1$ and $\sigma_2$  obtained by two-moment chi-square approximations \cite{WCleveland1988,JSun1994,XWang2005}. T-type statistics is different from the conventional T-statistics because it uses the weighted average of the values near $s$ whereas conventional T-statistics uses simple average. Therefore, $T(s)$ and $T(s')$ can be correlated for our T-type statistics whereas the test statistics are independent to each other  in the conventional T-tests.
\subsection{FDR-controlled P-Map}
The P-Map is generated from multiple comparison controlled p-values derived from the distribution of the T-type statistics. The most important problem in generating P-Map is overcoming the multiplicity problem for simultaneous comparisons.  Family-wise error rate (FWER) such as Bonferroni correction and false discovery rate(FDR) are the most common methods used to control for multiple comparisons. Section 4.2 of \textit{Zhang(2005)}\cite{ZZhang2005} discussed the relationship between FWER and FDR and we decided to use FDR for \pkg{LISA}. Specifically, we used the Benjamini and Hochberg's FDR-controlled  method \cite{YBenjamini1995}. The details of the method are introduced in section 4.2 Multiple testing problem of \textit{Wang et al.(2006)}\cite{XWang2006}. \pkg{LISA} provides `2D' and `3D' options in displaying the P-map.

\section[Example]{Application}
In this section, an application of \pkg{LISA} on Neuromuscular Electrical Stimulation(NMES) experiment \cite{NBergstrom2004} is introduced. NMES is a treatment proposed to improve pressure distribution at the seating or lying support area for patients with mobile difficulties. Patients are at a risk of developing pressure ulcers when seated or lying down for a long time due to the blocking of the blood flow. NMES is intended to improve the blood flow for healthier regional tissue. In this application, the effect of NMES is evaluated by comparing the sequences of the pressure mat images before and after the treatment. A step by step process of using \pkg{LISA} will be introduced to conduct the comparison.\\

Generic \pkg{LISA} function : \\

\begin{lstlisting}
  lisa('filename1','filename2','scantype','nframe','nrow','ncol',
       'rowid','colid','preprocess','segtype','registtype','roi',
       'displaym','dimenpmap','parcomp')
\end{lstlisting}

\begin{longtable}{ l p{12cm} }
	\code{filename1} & Name of the csv file that contains the first sequence of images\\
	\code{filename2} & Name of the csv file that contains the second sequence of images\\
	\code{scantype} &  Data scanning method. Possible options include\\
	&\code{`row'} : using a row identifier (required arguments : nrow, rowid)\\
	&\code{`col'} : using a column identifier (required arguments : colid)\\
	&\code{`no'} : using a blank row\\
	\code{nframe} &Number of frames in each sequence\\
	\code{nrow} &Number of rows in one frame or the height of the image\\
	\code{ncol} &Number of columns in one frame or the width of the image\\
	\code{rowid} &A characters string that identifies the start of a frame\\
	\code{colid} &An integer number that identifies the column where the frame numbers are stored\\
	\code{preprocess} & Logical value to inform if the images have been pre-processed. Possible options include\\
	&\code{`T'} : True\\
	&\code{`F'} : False (default)\\
	\code{segtype} &Type of image segmentation. Possible options include\\
	&\code{`auto'} : De-noises the image by choosing a cutoff value to divide noise to the region of interest by using a mixture of normal distributions of the intensity values (default)\\
	&\code{`manual'} : De-noises the image through user-chosen cutoffs for the intensity values to divide noise to the region of interest\\
	\code{registtype} &Type of image registration. Possible options include\\
	&\code{`auto'} : Rotates and/or shifts images based on a mid-line found by a linear regression (no user interface is required) (default)\\
	&\code{`manual'} : Rotates and/or shifts images based on a line specified by two points selected manually by a user (the first click sets a reference point and the second click draws the reference line)\\
	\code{roi} &Select region of interest after image registration. Possible options include\\
	&\code{`T'} : True\\
	&\code{`F'} : False (default)\\
	\code{display} &Output option. Possible options include\\
	&\code{`basic'} : Only displays the P-Movie (movie of multiple comparison adjusted p-values) (default)\\
	&\code{`all'} : Displays O-Movies (movies of the original two sequences of images), R-Movies (movies of the registered sequence of images), D-Movie (movie of the curated difference), S-Movie (movie of the non-parametrically smoothed differences), T-Movie (movie of variance-adjusted smoothed differences), and P-Movie\\
	\code{dimenpmap} &Select the dimension of P-Movie. Possible options include\\
	&\code{2} : Two dimensions (default)\\
	&\code{3} : Three dimensions\\
	\code{parcomp} &Parallel computing. Possible options include\\
	&\code{`T'} : True\\
	&\code{`F'} : False (default)\\
\end{longtable}

\subsection{Read in Data}
The argument \code{`scantype'} takes one of the three possible values. When \code{scantype = `row'}, \pkg{LISA} finds the first row that matches \code{`rowid'}, then \code{`nrow'} rows after the \code{`rowid'} are stored as a frame. The search continues until the frame after the last \code{`rowid'} is read in. When \code{scantype = `col'}, \pkg{LISA} scans the csv file, and read in frames using the frame numbers found under the \code{`colid'} column. When \code{scantype = `no'}, \pkg{LISA} uses blank rows as row ids, and read in \code{`nrow'} rows after each blank row as individual frames. After all data are read in, \pkg{LISA} will have two arrays with \code{nframe} number of cells having \code{`nrow'} by \code{`ncol'} matrix of the intensity values in each cell.

\subsection{Image Processing}
A user can skip the image processing step if it's unnecessary by setting \code{`preprocess'} to \code{`T'}. Otherwise, a user can set \code{`preprocess'} to \code{`F'} to process the images as follows.

\subsection{Select Region of Interest}
\pkg{LISA} shows the first images of each sequence to select ROI as the first step of image processing. An example is displayed in Figure \ref{fig:1roi} and specific instructions on how to choose ROI are provided on the screen. A user needs to drag a square to roughly select the ROI. It is advised to choose a region large enough to enclose all ROI. Between the two regions selected from each sequence, the size of the larger region will be used to crop the images.

\begin{figure}[h!]
\begin{minipage}{.5\linewidth}
	\centering
	\subfloat[][Select ROI for 1\textsuperscript{st} sequence]{\includegraphics[width=6cm]{1roi1seq.png}\label{<figure1>}}
\end{minipage}
\begin{minipage}{.5\linewidth}
	\subfloat[][Select ROI for 2\textsuperscript{nd} sequence]{\includegraphics[width=6cm]{1roi2seq.png}\label{<figure2>}}
\end{minipage}
\caption{Example of ROI selection}
\label{fig:1roi}
\end{figure}

\subsection{Image Segmentation}
In the image segmentation step, noises to ROI are identified using the pixel intensity values. Automatic and manual image segmentation options are available in \pkg{LISA} as described in section 3.3. The following is an example. \\

(Automatic segmentation)
\begin{enumerate}
	\item {Select ROI : A user can use a multi-point polygon to select detailed ROI as shown in Figure \ref{fig:segroi}.}
	\begin{figure}[h!]
		\begin{minipage}{.5\linewidth}
			\centering
			\subfloat[][Select detalied ROI for 1\textsuperscript{st} sequence]{\includegraphics[width=6cm]{autoseg1seq1.png}\label{<figure1>}}
		\end{minipage}
		\begin{minipage}{.5\linewidth}
			\subfloat[][Select detailed ROI for 2\textsuperscript{nd} sequence]{\includegraphics[width=6cm]{autoseg1seq2.png}\label{<figure2>}}
		\end{minipage}
		\caption{Example of ROI selection for automatic image segmentation}
		\label{fig:segroi}
	\end{figure}
	\item {Select groups for mixture of Gaussian distribution : A user can change the number of bins and define visually distinct groups from the distribution of intensity as shown in Figure \ref{fig:seggaussian}.}
	\begin{figure}[h!]
		\begin{minipage}{.5\linewidth}
			\centering
			\subfloat[][Select groups for 1\textsuperscript{st} sequence]{\includegraphics[width=6cm]{autoseg2seq1.png}\label{<figure1>}}
		\end{minipage}
		\begin{minipage}{.5\linewidth}
			\subfloat[][Select groups for 2\textsuperscript{nd} sequence]{\includegraphics[width=6cm]{autoseg2seq2.png}\label{<figure2>}}
		\end{minipage}
		\caption{Example of groups selection for automatic image segmentation}
		\label{fig:seggaussian}
	\end{figure}
\end{enumerate}

(Manual segmentation)
\begin{enumerate}
	\item {Pick cutoff value : A user should select one cutoff value from the histogram of the intensity values to distinguish the noise and ROI as shown in Figure \ref{fig:segcut}.}
	\begin{figure}[h!]
		\begin{minipage}{.5\linewidth}
			\centering
			\subfloat[][Select cutoff value for 1\textsuperscript{st} sequence]{\includegraphics[width=6cm]{manusegseq1.png}\label{<figure1>}}
		\end{minipage}
		\begin{minipage}{.5\linewidth}
			\subfloat[][Select cutoff value for 2\textsuperscript{nd} sequence]{\includegraphics[width=6cm]{manusegseq2.png}\label{<figure2>}}
		\end{minipage}
		\caption{Example of selecting cutoff value for manual image segmentation}
		\label{fig:segcut}
	\end{figure}
\end{enumerate}

\subsection{Image Registration}
Image registration can be done automatically or manually as described in section 3.4. Automatic registration does not require any additional input from a user. Manual registration requires a user to select two points : a reference point and a second point to form a reference line using the first image of each sequence as shown in Figure \ref{fig:manureg}.

\begin{figure}[h!]
	\begin{minipage}{.5\linewidth}
		\centering
		\subfloat[][Select reference point and line for 1\textsuperscript{st} sequence]{\includegraphics[width=6cm]{manuregseq1.png}\label{<figure1>}}
	\end{minipage}
	\begin{minipage}{.5\linewidth}
		\subfloat[][Select reference point and line for 2\textsuperscript{nd} sequence]{\includegraphics[width=6cm]{manuregseq2.png}\label{<figure2>}}
	\end{minipage}
	\caption{Example of points selection for manual image registration}
	\label{fig:manureg}
\end{figure}

\subsection{Image Alignment Check}
Before conducting statistical comparison, \pkg{LISA} overlaps the first images of the two sequences which have been processed to confirm whether the image segmentation and image registration is satisfied by a user. If a user is satisfied and selects `Yes', then \pkg{LISA} continues to statistical analysis but if a user selects `No' \pkg{LISA} provides suggestions to the user on image processing in a pop-up window and terminates the process. An example of the overlapped image is shown in Figure \ref{fig:overlap}.

\begin{figure}[h!]
	\centering
	\includegraphics[width=6cm]{overlap.png}
	\caption{Checking image alignment before statistical analysis}
	\label{fig:overlap}
\end{figure}

\subsection{Output Movies}
Depending on the option a user selects, \pkg{LISA} can provide 8 different movies :
\begin{enumerate}
	\item O-movie 1 (Figure 17-a) : Movie of the actual images from the first sequence
	\item O-movie 2 (Figure 17-b) : Movie of the actual images from the second sequence
	\item R-movie 1 (Figure 17-c) : Movie of the registered images from the first sequence
	\item R-movie 2 (Figure 17-d) : Movie of the registered images from the second sequence
	\item D-movie (Figure 17-e) : Movie of the curated difference
	\item S-movie (Figure 17-f) : Movie of the non-parametrically smoothed difference
	\item T-movie (Figure 17-g) : Movie of the T-type statistics
	\item P-movie (Figure 17-h) : Movie of FDR-controlled p-values
	\end{enumerate}

When \code{`basic'} is selected only P-map will be generated but when \code{`all'} is selected, all 8 movies will be generated for display. In D-movie, S-movie and T-movie, a blue peak signifies positive differences which indicate higher pressure in the first sequence compared to the second sequence, whereas, a red peak signifies the opposite. In P-movie, red areas are statistically significantly activated/different areas. The snapshots of the movies resulting from our data application using the seated pressure mats are introduced in Figure \ref{fig:results}.\\

\begin{figure}
	\centering
	\subfloat[O-movie 1]{\includegraphics[width = 3.5cm]{movie1.png}}
	\subfloat[O-movie 2]{\includegraphics[width = 3.5cm]{movie2.png}}
	\subfloat[R-movie 1]{\includegraphics[width = 3.5cm]{movie3.png}}
	\subfloat[R-movie 2]{\includegraphics[width = 3.5cm]{movie4.png}}\\
	\subfloat[D-movie]{\includegraphics[width = 3.5cm]{movie5.png}}
	\subfloat[S-movie]{\includegraphics[width = 3.5cm]{movie6.png}}
	\subfloat[T-movie]{\includegraphics[width = 3.5cm]{movie7.png}}
	\subfloat[P-movie]{\includegraphics[width = 3.5cm]{movie8.png}}
	\caption{Output movies}
	\label{fig:results}
\end{figure}

\section[Discussion]{Discussion}
\pkg{LISA} is an image analysis package that processes and analyzes two sequences of images to find significant activation regions between them. In \pkg{LISA}, the essential components of image processing functionality include ROI selection, image segmentation and image registration and that of statistical analysis functionality include non-parametric mapping, smoothing and multiple comparison adjusted testing.

The current version of \pkg{LISA} was developed for and focused on sequence of images from seated pressure mats but the package can be extended to analyze other types of time series images such as satellite images, hyper-spectral chemical images, longitudinal magnetic resonance images. The interactive function during the image processing step makes it easier for users to use the package and provides users with visual evidence to derive more accurate image alignment and statistical comparison results. Statistical analysis is designed to reduce possible errors resulting from edge effects and to account for multiple comparisons using false discovery rate which makes the final result conservative and accurate. \pkg{LISA} also has a parallel computing function to help with computing issues for big data. High-resolution or long image sequences can be analyzed with the parallel computing function in a multi-core processing environment.

Depending on a user's needs and preference, other image processing tools can enhance the overall analysis experience, such as the Image Processing Toolbox(IPT) within \proglang{MATLAB}. Among the many options in IPT, a function such as multi-point registration could help users to derive more accurate image alignments depending on the characteristics of the image. The underlying method is identical to manual registration in \pkg{LISA}, but users can select multi-reference points for image translation. For details, please refer to the MathWorks website \cite{matimageregist}. Any other tools can be used for image processing as long as the images are well aligned and the results are saved in `csv' format to be consumed by \pkg{LISA}.

Development of a standalone \proglang{MATLAB} package \pkg{LISA} as an ensemble of elements from the LASR algorithm \cite{XWang2006} with additional features for image processing and graphical user interface is the start of our project in building an image comparison analysis package for large dynamic sequence of high-resolution images. The current version can handle relatively simple and static sequence of images. However, we aim to develop a next generation of \pkg{LISA} to be able to handle more complex and dynamic images, such as the entire body of a human and comparing images at many different time points. If we take a pressure map of a entire body of a human as an example, our future work will focus on segmented image processing and relevant statistical comparisons. This will allow us to compare and find significant activation regions independently separating by upper body, lower body, arms, legs and for any specific areas.

Another area of our future development includes reducing the computing time. Even with the parallel computing option, the computing time for \pkg{LISA} can be relatively long depending on the size of the image and sequence and the number of cores in the computer because the image comparison is done pixel-by-pixel. Pixel-by-pixel comparison is the most comprehensive and exhaustive way to compare images but can also provide most accurate results relative to using measures. Therefore, we will be aiming to develop methods such as to distribute the statistical calculation to reduce the computing time working under the essential framework of pixel-by-pixel comparison.

\newpage
\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
FMCG »
Patanjali Ayurveda Ltd
food processing unit
Dhar district
Acharya Balkrishna
Patanjali to invest Rs 500-crore for food processing unit in Madhya Pradesh
MP government recently cleared 400 acres of land at Rs 25 lakh per acre to Patanjali Ayurveda at Pithampur, said MP IFCL chairman Mohammed Suleman.PTI | August 05, 2016, 08:32 IST
Madhya Pradesh government recently cleared 400 acres of land at Rs 25 lakh per acre to Patanjali Ayurveda at Pithampur.
BHOPAL: The Madhya Pradesh government has managed to woo Baba Ramdev-led Patanjali Ayurveda Ltd to set up a Rs 500-crore food processing unit at Dhar district in the state.
"We had learnt that Maharashtra government was eager to give 400 acres of land to Patanjali for the food processing project in Nagpur. But our officials did not lose heart...kept wooing (MD) Acharya Balkrishna to come to MP and invest Rs 500 crore in the food processing project," a top official of the Madhya Pradesh Industries Department said.
Our efforts have paid off, he added.
"We have demarcated 400 acres of land to Patanjali Ayurveda in Pithampur Industrial area spread over an area of 7,000 acres in Dhar district and have got an advance of Rs 1 crore from the company," Indore MP Audyogik Kendra Vikas Nigam Managing Director Kumar Purushottam told PTI today.
Madhya Pradesh government recently cleared 400 acres of land at Rs 25 lakh per acre to Patanjali Ayurveda at Pithampur, said Madhya Pradesh Trade and Investment Facilitation Corporation Limited Chairman Mohammed Suleman.
Tax benefits will be given to the company according to our policies and rules, when it starts production, he said amid whispers that the BJP government in the state has gone out of the way to offer sops to Patanjali.
The company has been asked to start production in three years. Acharya Balkrishna of the Patanjali Ayurveda is in touch with us, he said.
"The food processing project is going to provide direct employment to around 700 people," the senior IAS officer said.
Two months ago, Balkrishna, in a chopper, had scouted for locations to set up the food processing unit in MP.
Tags : Food & Entertainment, FMCG, Ayurveda, tax benefits, Patanjali Ayurveda Ltd, food processing unit, Dhar district, Baba Ramdev, Acharya Balkrishna


A quick down memory lane, in 2011, a report suggested that by 2015, purchasing power of black could hit $1.1 trillion, however this whooping figure would never be felt by black communities, another reason why you should consider supporting black businesses. You might also be wondering how to go about this. Well here's how.
Figure out where they are: Probably the most important step to take is to know where exactly black owned businesses are situated in your area of residence. This should be a no-brainer and would make patronage easier. You can google sites at your convenience using this format "Black owned businesses in Oklahoma". That should do the trick.
Make use of apps: You don't need to be tech savvy to leverage on mobile apps. I'm not going to promote or endorse any app in particular but I'm quite aware that there are mobile apps available on the apple store or android play store that makes it seemless to find black owned businesses around you. There are even some that notifies you when a new establishment hits town.
Online shopping: Whether it's scouting for the best hair products, coolest shoes, or trending styles, shopping online has made things seamless. So you shouldn't complain about how terribly far that black owned business is located. You can patronize other black owned businesses that have online stores.
Social media: Social media is a great tool to spread the word. We've got a problem of choosing one to stick with actually, as there are quite a number to choose from. However, the trio, Facebook, Twitter and instagram are the most influential at the moment. Make use of ad campaigns, hashtags and images to promote black owned businesses. Spread the word.
Host events there: Perhaps you've got something brewing up like your anniversary, wedding, birthday party or whatever, an event centre is always needed right? So what's the harm in booking a venue where a black owned business is located. Always look out for establishments that are close to you.


text:
Work as is it becoming…
Entrepreneurship means never having to ask permission.™
Welcome to Viewshift.net
About "Work as I Find it"
PARADIGM RESEARCH
Private: Surfing the Waves of Change
LIFE AS I FIND IT
WORK AS I FIND IT
MEANING AS I FIND IT
#FLS 3 – Silicon Life
April 23, 2018 #FrictionLessSociety, Innovation, Natural Growth, Paradigm Shifts, Science, SocietyRobert Harris
#FLS 3 – Will We Become The Dinosaurs of CARBON life After Silicon Life Takes Over?
Paul McCready said "yes" in 1998. Was he right? Experience his authentic big-picture wisdom in a rare concurrent video-voice-text TED talk along with my essay commentary.
Carbon vs Silicon
I confess, I never heard of Paul McCready until I decided to write this essay. I stumbled on him searching for a visual to complete this Twart on the existential war between Carbon and Silicon for dominance of earth.
What, you didn't know a war was going on? Sure, Silicon is our friend right now; look at all it does for us carbon life forms. But friends can become enemies before we know it, especially friends that share many of the same characteristics like Carbon and Silicon do; they are mates who have been very close sexy cousins (see excerpt below) for billions of years. (They both apparently sleep around with a lot of other elements.)
[Tutorial Interruption, sorry; skip if you brain doesn't care.] "Carbon and Silicon both have a so-called valence of four--meaning that individual atoms make four bonds with other elements in forming chemical compounds. Each element bonds to oxygen. Each forms long chains, called polymers. In the simplest case, carbon yields a polymer that is a plastic used in synthetic fibers and equipment. Silicon yields polymeric silicones, which we use to waterproof cloth or lubricate metal and plastic parts. But when carbon oxidizes--or unites with oxygen say, during burning--it becomes the gas carbon dioxide; silicon oxidizes to the solid silicon dioxide, called silica [or sand], one basic reason why it cannot support life [as we know it]."
Star Trek was already suspicious of Silicon 31 years before McCready's personal pronouncement, and that was 51 years ago now in 2018! But you say that was just science fiction, not reality. Really? It's worth second look since humanity has become steeped in silicon.
Star Trek explored the idea of a silicon life form in a 1967 TV episode The Devil in the Dark about an underground silicon monster named Horta that lived in tunnels. Horta looked like a huge hunk of hamburger that could shuffle along the ground–a laughable image for a monster. (Hmm …to shoot and kill it, or; to cook and eat it? What choice would Kirk and Spock make?)
Based on my compulsive crack research the word Horta probably came originally from the Biblical word "Hor" for mound or mountain, which later also came to mean pregnant. With only primitive special effects technology at the time, Star Trek's wacky idea was converted into a thoughtful TV script and highly-rated timeless drama in just three days by famed writer and producer, Gene L. Coon, in the early days of TV. But I digress.
McCready's TED Talk
Dr. Paul McCready was a deep-thinking problem solver, a much-celebrated innovative scientist and engineer, who was the first person to fulfill Leonardo da Vinci's dream of a human flying under his own power. McCready was a first-rate technologist noted for finding innovative ways to do more with less based on his profound understanding of the awesome innovative power of nature.
He passed away in 2007 at the age of 81. In his 1998 TED Talk he summed up his view of how humanity and its technology fit in with the rest of the natural world. He spoke in an informal conversational tone without notes, along with a few key slides. His theme throughout was the growing power humans were amassing over nature, which was two decades before our current hyper-connected wireless silicon-based FrictionLessSociety (FLS).
As Dr. McCready got to the end of his talk, he paused as if asking his audience for their permission to let him read his conclusions, i.e., "to paint his version of the Big Picture."
He began: "At last, I put in three sentences and had it say what I wanted."
Dr. McCready reads: "Over billions of years on a unique sphere, chance has painted a thin covering of life — complex, improbable, wonderful and fragile. Suddenly, we humans — a recently arrived species, no longer subject to the checks and balances inherent in nature — have grown in population, technology and intelligence to a position of terrible power. We now wield the paintbrush. And that's serious: We're not very bright. We're short on wisdom. We're high on technology. Where's it going to lead? There is no planet B."
He continued talking informally now, almost imploring the audience to help answer his question.
"This is not a forecast. This is a warning, and we have to think seriously about it. And that time when this is happening is not 100 years or 500 years. Things are going on this decade, next decade; it's a very short time that we have to decide what we are going to do. And if we can get some agreement on where we want the world to be — desirable, sustainable when your kids reach your age — I think we actually can reach it.
(Is this why kids today seem so angry with prior generations, for not having dealt with the negative consequences of what we created?)
McCready closed his formal remarks with this sobering thought:
"…Maybe this is a forecast, afterall…I personally think the surviving intelligent life form on earth is not going to be carbon-based; it's going to be silicon-based."
And then, with a sheepish grin, he delighted his audience and lifted their spirits "with one final bit of sparkle" by giving a live autonomous flight demonstration over the audiences' heads of an an "utterly impractical flight vehicle, a little ornithopter wing-flapping rubber-band powered device that weighed one gram."
It got stuck in the rafters like a mechanical moth fluttering its wings, but going nowhere. Everyone laughed.
Living in a Blur of Bits
Dr. McCready did not use the phrase "Blur of Bits" nor Frictionless Society (FLS) to describe the future as he saw it from twenty years ago, but he might have. This is what it looks like to me.
Based on his talk, he was prescient in his warning, and perhaps even in his forecast as well, because "Silicon" arguably has a "life of its own" in billions of Carbon Lives today. It pervades every aspect of society as data-being-processed:
Who you are, what you value, where you are, what you are doing, how you are doing, what and when you plan to do it, and why.
CEO Ginni Rometty stated in her opening remarks at IBM's February Think Conference that "only 20% of the world's data is currently searchable," implying that the other 80% is available for economic growth by all kinds of businesses large and small. IBM's plan? Everyone will use Watson's AI to monetize this data to solve problems for society (and move IBM back to its rightful place at the top of the Silicon Palace).
"Watson took a year to figure out it's first cancer; now it takes one month. It gets smarter and smarter. Programming a computer is dead; deep AI learning with data is the future." (Reminds me of the hammer and nail view of reality.)
The existential problematique of the FLS is that data, as information, is a complex chameleon of abstract potential. In physical form it is a liquid lizard that can change shape, color, size, dynamics, and merge seamlessly with other information to produce endless variety. It's the digital genie in the bottle we have let loose without thinking carefully about what wishes we want to be granted, which are turning out to be endless!
Is this what Carbon wants, or what Silicon wants?
@viewshift1
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Today's focus is on open adoption. We have four children, all of whom have open adoptions with their birth families. Each relationship is different, each experience is different, each dynamic is different. Now, let's get to those questions!
Photo of one of my kiddos kissing her bio brother on the cheek, many years ago. So precious!
Q: Our child's birth parents have cancelled several visits last-minute, and I'm tired, frustrated, and confused. What do we do about this?
A: I believe in keeping your promises, but I also believe that your #1 job is to protect your child. With that said, if the cancellations are disruptive to your child (emotionally), perhaps it's time to stop scheduling visits for a season. However, a difficult season is not your permission slip to bail on the relationship.
Q: We're getting ready to begin our adoption journey, and we don't know what to promise in terms of openness. I mean, we haven't even met our future child or his/her family yet! Our agency is pushing us toward more openness, but we just aren't sure.
A: I believe in short-term promises. Meaning, don't promise a visit once a year until the child is eighteen. Why? Because the CHILD will eventually have (and should have) say so in the openness. Now, I believe in a COMMITMENT to open adoption, but I don't believe in making concrete guarantees beyond the short-term. I also believe in letting the relationship develop organically and over time.
Q: What does open adoption require of the parents?
A: Grace, empathy, honesty, flexibility, and commitment. Open adoption is NOT easy; it has both its joys and challenges. Open adoption is an ARC and not a marathon. Also, open adoption isn't for every person and every adoption. That's OK. Just because open adoption is popular (and pushed upon birth and adoptive families by adoption agencies and attorneys), doesn't mean it is the correct avenue for every person.
Q: Doesn't open adoption confuse the child?
A: Not in my experience. Lots of kids have family dynamics that aren't the "norm" (a mom, a dad, who are married, and bio siblings, all living under the same roof), and it's not confusing as long as parents are open and honest. There are some great children's books that explain family dynamics which can help you explain to your child what his or her family is like.
Q: I think we should choose open adoption, my partner doesn't. How do we decide what to do?
A: I think it's SO difficult to say what's best when have yet to meet your future child and his or her family. Choosing open adoption before adopting is almost like that TV show Married at First Sight. You make a life-long promise to someone who is a stranger? That said, I do think your job right now is to get educated. If you sign up for my e-newsletter, you'll be sent a free e-book that explains why education is critical! Learn all you can about open adoption prior to adopting so when you are presented the opportunity to adopt a child, you can make your decisions out of education and not ignorance.
What are your thoughts on open adoption? Let's chat on Facebook.


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Find more information at https://www.zotero.org/support/sorting.


Colin is a Partner in the Technology & Innovation Group of Arthur Cox. Colin advises on data privacy, freedom of information and information technology matters. Colin's practice has a strong emphasis on information management issues, ranging from cross-jurisdictional data sharing projects to digital marketing to online regulation.
Colin also regularly advises in relation to various aspects of technology law (for example regarding cloud computing, software licensing, social media and e-commerce) and on commercial intellectual property matters (for example regarding licensing and migration of valuable intellectual property). Colin advices Irish and international companies, including publicly listed corporations, privately owned companies and State bodies.
Colin is a frequent speaker and author on data privacy topics.
Colin is a Certified Information Privacy Professional (Europe) (CIPP/E) and a Certified Information Privacy Manager (CIPM).


Speidel is a medium-size enterprise with over 120 employees. For generations we have been passionately manufacturing containers in our headquarter in the Swabian town of Ofterdingen.
Our long-time expertise has become second nature to us. Since the beginning, Speidel tanks have been known for their outstanding quality and longevity.
In order for our proverbial quality to remain the benchmark of our work, we invest in the most modern technology and manufacturing plants as well as in the best training possible of our employees. This is also the reason why we continue manufacturing our tanks in Germany. And our clients know to appreciate that.