Image copyright White Star Image caption Sabeen Mehmud had been the subject of threats before being killed in Karachi

A leading Pakistani human rights activist has been killed in a drive-by shooting in Karachi after hosting a talk on allegations of torture in the province of Balochistan.

Sabeen Mehmud was shot dead as she drove home with her mother, who was also attacked.

Ms Mehmud had been the subject of death threats before.

Tributes were paid to her on social media as soon as news of her death emerged.

Ms Mehmud was a director of the charity The Second Floor, also known as T2F.

T2F regularly holds seminars on human rights issues. It houses a cafe and book shop where Karachi's liberal activists and students can meet.

The seminar on torture in Balochistan was held at T2F, having been cancelled by university authorities in Lahore, where it had been due to take place in the last few weeks.

Taliban militants, Baloch separatists and other groups fight in Balochistan, which borders Iran.

Shortly after leaving the event, Ms Mehmud and her mother were shot. Pakistan's Dawn newspaper reported that Ms Mehmud died on her way to hospital, and that she had been shot five times.

Dawn reported that her mother is in a critical condition in hospital.

'An extraordinary life' - by Fifi Haroon, BBC Urdu

When you don't see that many people stand up, and you do, you are very likely to stand out. Sabeen knew that and went ahead and did it anyway.

It's a huge thing when you have been told to call off an event, only to go ahead and do the same event in a public space, when it's so dangerous in Pakistan.

It is difficult to believe someone so vibrant, so alive, so determined is gone. There is a disbelief in Pakistan, on social media and from young people.

To me, she was someone who could have chosen to lead a very different, ordinary life. But she chose to lead an extraordinary life.

Within an hour of the attack, tributes had started appearing on social media.

Image copyright Twitter

Image copyright Twitter

Image copyright Twitter

Among the tributes was one from Siraj ul Haq, the head of the conservative Islamist group Jamaat-e-Islami, who tweeted: "Murder of Sabeen Mahmud is shocking & horrific. It is extremely condemnable, indeed coward of them to target a woman."

No group has yet said it carried out the attack, though Ms Mehmud is believed to have been the subject of threats by the Pakistani Taliban in the past.

Image copyright Instagram Image caption Sabeen Mehmud posted a photograph from inside Friday's talk shortly before being attacked

Ms Mehmud set up T2F having already established a charity, PeaceNiche, in Karachi. She said she "maxed out seven credit cards" to keep the centre going.

She also helped promote the importance of learning computer skills among Pakistani youth, and hosted hundreds of events at T2F.

In a profile in a Pakistani media magazine in 2013, Ms Mehmud was asked what her superpower would be. She answered: "I'd like to wave my magic wand and de-weaponise Karachi."


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A survey of adult and pediatric epilepsy surgery in the United Kingdom.
All consultant epilepsy neurosurgeons were asked to prospectively record all epilepsy surgery procedures carried out at their center between April 2010 and March 2011. Figures were compared to a previous survey completed in 2000. Of a total of 710 procedures, temporal lobe surgery was the most common resective surgery. Although extratemporal lesional surgery was less common, vagus nerve stimulator (VNS) implantation accounted for almost half the procedures. The numbers for all surgical procedures, with the exception of VNS implantations, had decreased. This decrease may represent a global rather than a regional phenomenon. Further longitudinal multinational data on epilepsy surgery is required to confirm or refute this theory.


Q:

Apple Vision create custom VNImageBasedRequest

I've been playing with Apple's CoreML and Vision APIs.
My goal would be to make a simple proof of concept and be able to recognize nails on a hand picture. This is very specific.
I have been trying to find documentation on how to create your own VNRequest, and I really have no idea on how to do this.
I know that the Vision API offers rectangle, face and text recognition only...
How can I make my own request to teach Vision how to recognize what I want on a picture ?

A:

You will have to create (or find) a Core ML model that can do this. There is at least one open source model that can detect nails, so you'd have to convert this to Core ML. And then you use VNCoreMLRequest to run this model using Vision.


#Mon Oct 17 16:40:13 PDT 2016
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-4.4-all.zip


Information theory has been extensively applied to neuroscience problems. The mutual information between input and output has been postulated as an objective, which neuronal systems may optimize. However, only recently the energy efficiency has been addressed within an information-theoretic framework \[[@B1]\]. Here, the key idea is to consider capacity per unit cost (measured in bits per joule, bpj) as the objective. We are interested in how biologically plausible constraints affect predictions made by this new theory for bpj-maximizing model neurons.

More specifically, in our contribution, in line with \[[@B1]\] and \[[@B2]\], a neuron is modeled as a memory-less constant communication channel with a Gamma conditional probability distribution function (PDF) \[[@B1]\]. In this setting, the channel input and output are the excitatory postsynaptic potential intensity, $\ \lambda$, and the inter spike interval (ISI), $\ t$, with PDFs $f_{\Lambda}\left( \lambda \right)$ and $f_{T}\left( t \right)$, respectively. We then formulate two new constraints: First, we impose a lower bound $t_{\textsf{min}}$ on the duration $\ t$of ISIs. The rational for this is to account for a maximal firing rate. Second, we consider a peak energy expenditure constraint per ISI as compared to only bounding the expected energy expenditure. This translates into an upper bound $t_{\textsf{max}}$ on the ISI duration. We then derive the $f_{T}\left( t \right)$ (corresponding to valid $f_{\Lambda}\left( \lambda \right)$) of a bpj-maximizing neuron for the original unconstrained setting from \[[@B1]\] and in the presence of the above two constraints for different expected ISIs. (Details omitted here for brevity.) Figure [1](#F1){ref-type="fig"} shows three $f_{T}\left( t \right)$s obtained in the unconstrained (dashed curves) and constrained settings (solid curves) for $t_{\textsf{min}} = 1$and $t_{\textsf{max}} = 5$. While the constrained and unconstrained solutions have the same mean, the shape of their $f_{T}\left( t \right)$ differ. For comparison with experimental data, we computed the coefficient of variation (CV) as a function of the mean ISI as an \"observable\" (Figure [2](#F2){ref-type="fig"}), which is easier to measure experimentally than the full distribution $f_{T}\left( t \right)$. Interestingly, the CV is predicted i) to be lower in the constrained setting, and ii) to *increase*and then decrease with the mean ISI while it only *decreases*in the unconstrained setting. Thus, we demonstrated that constraints can affect predictions based on bpj-maximization, and should be explicitly taken into account. Ongoing work makes these predictions more quantitative via simulating biophysically realistic model neurons.

![](1471-2202-14-S1-P349-1){#F1}

![](1471-2202-14-S1-P349-2){#F2}

Acknowledgements
================

This research has been supported in part by the DAAD (German-Arabic/Iranian Higher Education Dialogue).


Hola que tal amigo de YouTube, bienvenido
a un nuevo video.
Si ya instalaste macOS Sierra (10.12) y en
tu Mac tienes un SSD de terceros (no original
de Apple), lo más probable es que tengas
que activar el soporte TRIM, para permitir
que todo funcione a la perfección. Cabe destacar
que este tutorial también funciona para Yosemite
y El Capitan.
Si instalaste un SSD por tu cuenta luego de
comprar tu Mac, lo primero que debes hacer
ahora es habilitar el soporte TRIM. Pero ¿qué
es TRIM? “En informática, una orden TRIM
permite a un sistema operativo comunicarle
a una unidad de estado sólido (SSD) qué
bloques de datos ya no están en uso, como
los datos dejados al borrar ficheros. Una
operación del SO como un borrado generalmente
sólo marca los bloques implicados como no
usados. TRIM permite que el SO pase esa información
al controlador de la SSD, que de otra manera
no sabría qué bloques puede eliminar. El
propósito de la instrucción es mantener
la velocidad de la SSD durante toda su vida
útil, evitando la ralentización que los
primeros modelos sufrían al llegar a escribir
en todas las celdas al menos una vez”. [Fuente
WikiPedia].
En pocas palabras y para resumir lo anterior,
ten en cuenta que si no habilitas esta opción,
el disco SSD que tengas en el Mac no trabajara
al máximo de su capacidad. Luego no te quejes
si ves que el equipo se torna lento. Por lo
cual, siempre, siempre que instalar una unidad
SSD en tu ordenador Apple, activa el soporte
TRIM.
Activar soporte TRIM para discos SSD en macOS
Para activar esta opción existen aplicaciones
como Trim Enabler, pero la forma más sencilla
–desde mi punto de vista– viene a través
del terminal y un simple comando.
Primero que todo verifica desde la ‘Manzana’
> ‘Acerca de este Mac’ > ‘Informe del
Sistema’ > ‘SATA/SATA Express’ > ‘Compatibilidad
con TRIM’ si la opción está desactivada
o no.
Para activarla abre el terminal y pega el
siguiente comando (sin comillas):
“sudo trimforce enable”
Pulsa enter, ingresa tu contraseña y pulsa
nuevamente enter. Espera unos segundos e indica
a través de la letra ‘y’ que quieres
continuar. Por último permite que reinicie
el Mac con la letra ‘y’ nuevamente y espera
a que se reinicie el equipo.
Cuando encienda nuevamente ve a ‘Manzana’
> ‘Acerca de este Mac’ > ‘Informe del
Sistema’ > ‘SATA/SATA Express’ > ‘Compatibilidad
con TRIM’ y verifica que sí esté activo.
En caso que quieras desactivar la opción
TRIM aquí te dejo el comando, el procedimiento
es el mismo que ya observaste:
“sudo trimforce disable”
Así de fácil ya puedes empezar a disfrutar
por completo de los beneficios de tener un
disco SSD. Explora al máximo las capacidades
de estos dispositivos ahora en macOS Sierra.
Espero este tutorial te sea de utilidad. Por
cierto si quieres instalar desde cero macOS
Sierra, aquí el tutorial.
Si el video te gustó ya sabes, dale like,
compartelo en tus redes sociales y suscríbete
para aprender más. Yo soy Keiner Chará y
este es tu canal.


Information bringing people together...
Maren's list of environmental, cultural, and social justice events in and around Pittsburgh.

May 17: Empowering Communities for a Healthy Tomorrow (part 3)

Empowering Communities: Part 3

Environmental Exposures and the Links to Reproductive Health

Latest scientific findings have linked many environmental exposures to reproductive health, including air pollution and endocrine disrupting chemicals, just to name a few. As part of WHE's maternal and child health programming, we are pleased to bring Dr. Marya Zlatnik, MD, MMS to Pittsburgh to speak about these connections between environmental toxins and reproductive health.

Dr. Zlatnik is Professor of Obstetrics, Gynecology, & Reproductive Sciences at UCSF, and a Maternal Fetal Medicine specialist, with clinical expertise in ultrasound and the diagnosis of fetal anomalies. She has a long-standing interest in environmental toxins and their effect on pregnancy. She is a member of UCSF's Program in Reproductive Health and the Environment (PRHE), as well as the associate director of UCSF's fellowship in Maternal Fetal Medicine. In 2014, she joined the University of California San Francisco's Pediatric Environmental Health Specialty Unit (UCSF PEHSU) as Associate Director, Maternal Fetal Health and the Environment Program.

7 - 9 p.m. (doors open 6:30) at the Carnegie Science Center's Science Stage (One Allegheny Avenue, Pittsburgh, PA 15212). This is the first of a three-part series. Register online for one ($10) or all three ($25) events.

Who is Maren?

Maren serves on the board of the Group Against Smog & Pollution, the Environmental Committee of the Pittsburgh Botanic Garden, and the steering committee of Transition Pittsburgh. She's a Tree Pittsburgh Tree Tender, a Phipps Conservatory Master Gardener, and a Pittsburgh Parks Conservancy Urban Ecosteward, and founded the Edible Schoolyard at Pittsburgh Colfax K-8. Maren has been closely involved in the Urban Farming Initiative, the Green Education Movement, Three Rivers Bioneers, the Urban Ecology Collaborative, the Pittsburgh Organizing Committee of the Business Alliance for Local Living Economies, Achievement Rewards for College Scientists, and Sierra Club’s Shale Gas Action Group.
Putting Down Roots is a source for Science & Environmental Education and Consulting, Sustainability Salons, Organic Production, Documentation, Graphics and Communications for Sustainable Communities... Local food systems, urban agriculture, permaculture, green building, and green business.
Maren can be reached by email at maren dot cooke at gmail dot com .


South Carolina Appeals – Tips and News

He, S/he, He or She, or They?

Last week, I drafted a trial brief that referred to a judge as “he.” The judge is a she. So is the judge I was writing. Thank goodness I check my pronouns for gender before my briefs go out.

But what if gender is indeterminate? Teachers taught me to use “he” to mean he and she. The venerable Strunk and White calls this a simple, practical convention rooted in the beginnings of the English language. And if Strunk and White says it, and my teachers taught it, it must be right. Right?

My grade-school schooling was a while back. Other than Strunk and White, major style guides now condemn using “he” generically. William Zinsser was also much more steeped in tradition than me when he changed his views on this. In On Writing Well, Zinsser explained that his readers nudged him toward more gender-neutral language. He cut 300-400 male pronouns from his book with no harm done.

How do you do that?

Switch to plural nouns

Zinsser largely used plural nouns to make the switch, allowing him to use the pronoun “they.” He noted, however, that plurals often weaken writing because they are less specific than the singular, less easy to visualize.

Use “he or she”

Using “he or she” is easier. Yet a leading legal style guide warns that the phrase usually sounds stilted and becomes obnoxious if used in excess. The Redbook § 12.5(n).

In The Elements of Legal Style, Garner calls this usage “inevitable” and praises it as a solution to a problem that has plagued the language for centuries. But he also cautions that judges may think us less credible if we use “they” this way. We are not Shakespeare.

Alternate he and she

Alternating “he” with “she” is another way to be more gender neutral. The State Legislature blesses this approach: “All words in an act or joint resolution importing the masculine gender shall apply to females also and words to the feminine gender shall apply to males.” S.C. Code Ann. § 2-7-30(A). But this risks whiplash. And Richard Wydick noted that we may wind up performing a sex change operation on somebody in the middle of a paragraph.

Rewrite the sentence

Other ways to avoid masculine pronouns includes avoiding personal pronouns altogether (repeat the antecedent noun or use the passive voice); neutering the terms (use “one,” “someone,” or “person”); or replacing personal pronouns with an article (the, a, or this).

Create words

Then there is “s/he,” “zir,” “thon” and other proposals that never made it into a lexicon. Efforts to create new, gender-neutral pronouns date back 150 years. None has succeeded.

Conclusion

What do you do? Do you say “he,” “he or she,” or avoid personal pronouns altogether? Please leave a reply or reach me at www.attorneyroberthill.com.


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vf.version=3


A novel is an invented story told through literature. Writers of novels are often referred to as novelists.

The novel is often defined as being a fiction in prose of a certain length--typically more than 50 000 words--with characters, incidents and perhaps a plot. This admits of counterexamples: many novels, among them Ulysses by James Joyce and Moby-Dick by Herman Melville, contain passages written in drama or verse form. Historical (non-fictional) novels, such as I, Claudius by Robert Graves are a well-established subclass of novels. Some, perhaps Rainer Maria Rilke's Notebooks of Malte Laurids Brigge, contain little or nothing in the way of a plot.

Prior to the rise of the novel very little work written in prose was taken seriously as artistic literature. People used prose for science, law, history, and philosophy, but the general attitude was that work written without poetry could hardly count as aesthetically interesting on its own. The early novel was meant to reach a large public and at the beginning it was considered as a lower form of literature - neither true nor beautiful. The novel is thus historically linked with realism in style and content, but this can no longer be considered generally true. The discovery of the wealth of aesthetic value that inheres in prose not constrained by poetic structures can be considered a major development in world literature.

What sets the novel apart from a short story is that it is longer, more complex, and deals with more than one issue in the lives of its characters. What sets it apart from a play is that it is not confined by the restrictions of the stage[?], human actors and the audience[?]. What sets it apart from poetry is that it is written in prose form.

Many novels fit easily into various genres or types. Some of the following examples are classes of genre fiction, that tend to set well-defined and systematic norms of subject matter and style; others are classifications of works by less obvious or fewer traits, or by quality.


(19).
280
Let n(o) = -o**3 + 34*o**2 - 88*o + 38. Calculate n(14).
2726
Let p(n) = 147*n - 13636. What is p(88)?
-700
Let g(x) = -2*x**2 - 602*x + 5. What is g(0)?
5
Let l(k) = -10784*k - 2836211. Determine l(-263).
-19
Let g(j) = -29*j**2 - 6389*j - 1956. What is g(-220)?
24
Let i(n) = -2*n**3 - 23*n**2 + 123*n + 1497. Calculate i(-14).
755
Let q(h) = -h**3 - 34*h**2 - 352*h + 517. Give q(-20).
1957
Let h(s) = -119*s + 354. Calculate h(-7).
1187
Let t(w) = -2*w**3 + w**2 + 171*w - 1578. What is t(11)?
-2238
Let g(y) = 5*y**3 + 252*y**2 - 679*y - 323. Give g(-53).
-853
Let h(l) = -12*l**2 + 35*l + 397. Determine h(10).
-453
Let f(k) = -8095*k + 49586. What is f(6)?
1016
Let s(f) = 21525*f - 43072. Give s(2).
-22
Let z(u) = 5*u**2 - 506*u + 6177. Give z(87).
0
Let c(s) = -2*s**2 - 52*s + 65171. Determine c(-194).
-13
Let n(m) = -384*m**2 + 36097*m - 128. Give n(94).
-34
Let p(f) = -f**3 + 42*f**2 - 158*f + 189. Determine p(38).
-39
Let g(h) = -28663*h - 200640. What is g(-7)?
1
Let f(p) = 28*p**2 + 3570*p + 289. Determine f(-127).
-1489
Let n(q) = 264*q**2 + 93462*q + 2157. Give n(-354).
33
Let g(s) = s**3 + 106*s**2 + 1112*s - 1517. Give g(-94).
-13
Let h(q) = 81530*q + 815223. What is h(-10)?
-77
Let v(j) = -8*j**2 - 6693*j - 2. Give v(0).
-2
Let s(x) = -x**2 + 850*x - 75143. Give s(750).
-143
Let t(a) = -2*a**2 + 22946*a + 45885. Calculate t(-2).
-15
Let x(a) = -117*a - 7650. What is x(-67)?
189
Let c(h) = 4390*h + 245770. Give c(-56).
-70
Let t(g) = 5*g**3 - 258*g**2 + 209*g - 2702. Calculate t(51).
154
Let r(a) = 5842*a - 5182. What is r(1)?
660
Let l(a) = -a**3 - 108*a**2 - 944*a + 210. Determine l(-98).
-3318
Let b(m) = 61*m - 15500. Give b(254).
-6
Let u(j) = -5*j**2 + 35*j + 461. What is u(-12)?
-679
Let h(a) = 2*a**3 - 293*a**2 + 4512*a + 362. Calculate h(129).
-25
Let u(i) = -56*i**2 + 1123*i + 15. Determine u(20).
75
Let t(m) = -4*m**2 + 1221*m - 91224. What is t(176)?
-232
Let m(i) = 1025*i + 37757. What is m(-35)?
1882
Let z(g) = -10*g**2 + 508*g + 11548. Determine z(-17).
22
Let r(u) = 2*u**3 - 46*u**2 - 2603*u + 59866. Determine r(23).
-3
Let l(u) = u**2 - 685*u + 117268. What is l(334)?
34
Let v(f) = 1091*f**2 + 4382*f + 77. Give v(-4).
5
Let i(l) = l**3 - 25*l**2 - 5254*l + 678. Calculate i(86).
-10
Let l(c) = -c**2 - 464*c - 12667. Give l(-32).
1157
Let q(y) = y**3 + 24*y**2 + 78*y - 34. Give q(-20).
6
Let c(s) = 3*s**3 - 23*s**2 - 331*s + 2421. Give c(7).
6
Let r(c) = -2*c**3 + 19*c**2 + 33*c - 213. Calculate r(11).
-213
Let u(l) = 52795*l - 475147. What is u(9)?
8
Let w(c) = c**3 - 364*c**2 + 8542*c - 22675. What is w(339)?
38
Let o(v) = -82*v**2 + 2155*v + 1648. Determine o(27).
55
Let t(v) = -v**3 + 237*v**2 + 34132*v + 2022. Calculate t(338).
-6
Let p(w) = -2*w**3 - 265*w**2 - 166*w - 1400. Calculate p(-132).
3088
Let u(w) = 6390*w - 172529. Determine u(27).
1
Let d(r) = 2*r**2 - 2331*r - 93949. Give d(-39).
2
Let w(x) = -144517*x - 2023242. Determine w(-14).
-4
Let g(n) = -8283*n + 74574. Determine g(9).
27
Let g(o) = -1919*o - 14072. What is g(-7)?
-639
Let w(n) = -18653*n - 6976222. Give w(-374).
0
Let g(a) = a**2 + 1930*a + 563916. What is g(-359)?
-73
Let v(q) = 4*q**3 + 16*q**2 - 45685*q - 182740. Give v(-4).
0
Let c(j) = -35163*j - 1406477. Give c(-40).
43
Let t(w) = -54*w - 4846. Calculate t(-83).
-364
Let l(f) = 99*f**2 - 1533*f - 78. What is l(16)?
738
Let h(j) = -3094*j**2 - 1484*j - 1485. Calculate h(-1).
-3095
Let p(y) = -8126*y + 8954864. Determine p(1102).
12
Let x(r) = r**2 + 4063*r - 1383775. Give x(316).
-11
Let v(k) = -171*k**3 - 4105*k**2 - 18*k + 52. What is v(-24)?
-92
Let t(x) = -10*x**2 - 402*x - 3789. Calculate t(-11).
-577
Let u(o) = -3514*o - 4457. Give u(-1).
-943
Let o(b) = b**3 + 314*b**2 + 123*b + 38628. Give o(-314).
6
Let a(b) = -85*b - 5415. Give a(-50).
-1165
Let u(z) = 7956*z + 6500. Determine u(-1).
-1456
Let t(w) = w**3 + 2371*w**2 + 214033*w - 488. What is t(-94)?
-18
Let a(f) = f**3 + 17*f**2 - 180*f + 302. What is a(-22)?
1842
Let i(t) = -9201*t - 63962. Determine i(-7).
445
Let g(m) = m**2 - 28711*m - 201030. Calculate g(-7).
-4
Let l(g) = -506*g + 5996. What is l(12)?
-76
Let s(p) = p**3 + 236*p**2 - 2467*p + 147. What is s(10)?
77
Let x(i) = 19*i**2 + 602*i - 25. Determine x(0).
-25
Let x(j) = j**3 - 73*j**2 - 83*j + 6106. Determine x(73).
47
Let j(n) = 10*n**2 - 876*n + 7755. Give j(10).
-5
Let q(n) = -n**3 + 460*n**2 - 38735*n + 68. Determine q(111).
512
Let y(w) = -3*w**2 - 812*w + 9573. Calculate y(-283).
-898
Let c(b) = b**3 - 539*b**2 + 1104*b - 16143. Determine c(537).
-33
Let q(v) = 342*v + 17906. Determine q(-53).
-220
Let n(v) = -v**3 - 31*v**2 - 4*v - 162. Give n(-31).
-38
Let v(b) = 17636*b - 88189. What is v(5)?
-9
Let q(v) = -v**3 - 1306*v**2 - 11664*v + 90. Give q(-9).
9
Let h(b) = 32818*b + 164001. Give h(-5).
-89
Let v(z) = -4*z**3 + 70*z**2 - 713*z + 11540. Give v(17).
-3
Let o(t) = 2586*t + 30588. Give o(-12).
-444
Let c(v) = -5*v**3 + 702*v**2 - 2109*v - 4. Calculate c(3).
-148
Let v(i) = -i**3 + 239*i**2 - 29*i + 6926. What is v(239)?
-5
Let r(s) = -7*s**2 + 287*s - 2378. Give r(12).
58
Let l(a) = 21*a**3 + 2*a**2 - 165*a - 348. What is l(-2)?
-178
Let z(a) = 2*a**3 + 2257*a**2 + 41057*a - 14437. What is z(-1110)?
-7
Let x(d) = 2304*d - 28171. Determine x(12).
-523
Let h(g) = -2*g**2 - 63*g + 192. What is h(19)?
-1727
Let b(j) = 7*j**3 + 34*j**2 + 57*j + 647. Give b(-6).
17
Let a(o) = -4303*o - 69103. Determine a(-16).
-255
Let u(i) = -6*i**3 + 198*i**2 - 14*i + 391. Determine u(33).
-71
Let g(d) = -13*d**2 - 4386*d - 25786. Calculate g(-6).
62
Let k(t) = -2601*t - 163809. Determine k(-63).
54
Let o(c) = 5*c**3 + 256*c**2 + 38*c - 5. Give o(-51).
658
Let o(x) = 35*x**3 + 5349*x**2 - 920*x - 304. What is o(-153)?
2
Let t(v) = -8*v**3 - 28*v**2 - 306*v - 1227. Determine t(-4).
61
Let o(h) = 44921*h + 224817. Calculate o(-5).
212
Let s(a) = -6239*a - 554821. Calculate s(-89).
450
Let f(o) = -206346*o - 1444395. Give f(-7).
27
Let o(d) = 3120*d + 52711. Give o(-17).
-329
Let p(x) = -97*x**2 - 4079*x - 258. What is p(-42)?
-48
Let g(c) = -6*c**2 + 928*c + 18636. What is g(-18)?
-12
Let f(a) = a**3 - 34*a**2 - 395*a - 7213. What is f(46)?
9
Let y(b) = -1492*b + 96987. Determine y(65).
7
Let k(c) = 80730*c + 4440135. Give k(-55).
-15
Let b(q) = -3070*q - 2714. Determine b(-1).
356
Let r(h) = -h**3 + 44*h**2 + 18*h - 602. Give r(44).
190
Let w(g) = -g**3 - 90*g**2 - 386*g - 3623. Calculate w(-86).
-11
Let k(i) = 524*i + 517. Determine k(-1).
-7
Let h(m) = m**2 - 35*m - 1679. Give h(-39).
1207
Let i(o) = o**2 - 140*o + 4521. Determine i(68).
-375
Let t(v) = -v**2 - 541*v - 15677. Calculate t(-31).
133
Let q(v) = 538973*v + 17247126. Give q(-32).
-10
Let l(x) = 5*x**2 - 2024*x + 1600. What is l(404)?
-16
Let c(r) = 236*r**2 - 251*r + 22. Calculate c(2).
464
Let p(f) = f**3 + 285*f**2 + 1559*f + 122237. What is p(-281)?
2
Let j(u) = 594*u + 153783. Give j(-259).
-63
Let m(l) = 64586*l - 645868. Give m(10).
-8
Let l(n) = -n**3 + 227*n**2 + 1307*n - 346468. Calculate l(226).
-10
Let c(f) = -11872*f + 736069. Determine c(62).
5
Let q(x) = 2*x**3 - 298*x**2 - 304*x + 1067. Determine q(150).
467
Let n(i) = -i**3 + 1121*i**2 - 61*i + 1327320. What is n(1122)?
-6
Let g(n) = -25*n**3 - 413*n**2 + 415*n - 2. What is g(1)?
-25
Let m(x) = 897*x + 3545. Determine m(-4).
-43
Let x(r) = -22415*r + 10826445. Determine x(483).
0
Let g(n) = 925*n + 358019. Determine g(-387).
44
Let d(w) = -16*w**2 + 3875*w - 147222. Give d(195).
3
Let n(f) = f**2 + 39541*f + 908957. Calculate n(-23).
43
Let a(u) = 212340*u + 3609746. What is a(-17)?
-34
Let s(u) = -30*u**2 + 9121*u - 465. What is s(304)?
-161
Let f(w) = w**2 - 141*w + 271. Give f(101).
-3769
Let l(h) = -7*h**3 - 977*h**2 - 3410*h - 1358. Calculate l(-136).
2
Let b(c) = 46*c**3 - 328*c**2 + 50*c - 31. Calculate b(7).
25
Let o(w) = -w**3 - 140*w**2 + 1338*w - 512. What is o(-149)?
-65
Let r(t) = 44507*t + 10993219. Give r(-247).
-10
Let y(t) = -17*t**2 + 33912*t - 67818. Calculate y(2).
-62
Let t(f) = -60752*f - 3037588. Determine t(-50).
12
Let y(i) = -15*i**3 - 4*i**2 + 61*i + 146. Give y(-5).
1616
Let h(n) = n**3 - 416*n**2 + 843*n - 5403. Determine h(414).
807
Let v(c) = -3107*c + 24834. Determine v(8).
-22
Let q(o) = -332806*o


Trump and the Republicans

I have avoided writing about current politics for several reasons. Firstly, because the situation so quickly changes, nothing you write today may hold for tomorrow. Secondly, because it is so touchy a subject, you risk alienating your reader for minor offenses that can be taken as index markers for major disagreements. Thirdly, because politics is such a minor part of what makes a difference in our individual lives; so many other things are more important and more interesting.

Nevertheless, the chaos of the current American situation calls for some small clarification. Arguments muddy when thinking is unclear.

To begin, there is the issue of Donald Trump, which is a great squirt of squid juice, obscuring more lasting problems. It is easy to make fun of the Great Pumpkin, he practically satirizes himself. While he has fervent supporters, it is hard to know exactly what he stands for, because his words are so vague in application, no matter how blunt in expression. It is always possible to assume he is your ally, because you only listen to those words that honk your horn. Is he conservative? Conservatives value free trade. Is he pro-business? Business has told him they need an immigrant workforce. What does he stand for besides ignorance?

He is an obfuscation on the surface, a chaos beyond that because, of course, he has no ideology, other than Trumpism. It is not his supposed conservatism that I object to; there have been many thoughtful conservatives. Trump is not one of them; he isn’t even a conservative at all. What scares the bejeezus out of me about him is that he is so clearly unbalanced mentally. The word Andrew Sullivan has used is “bonkers,” and that can hardly be improved for accuracy. The constant wheezing about his vote count, poll numbers, inauguration crowd, all spouted against obvious and visual evidence, is a clear indication that he is unmoored from reality.

Then, there are the speeches, barely in English. They are really just sentence fragments thrown together with unattached adjectives. Yuge, sad, unbelievable, disgusting. They, as Philip Roth has counted them, are constructed from a vocabulary of a mere 77 words, reused and rearranged ad hoc. They jump around from topic to topic with little or no segue. And then, they are filled with things that are demonstrably untrue. One watches over an over when Trump says he never said this or that, followed by the videotape of him saying exactly what he now says he never said. Does he not know that his words have been recorded?

It cannot be easily said that Trump is a liar, because a liar knows what he is saying is untrue. Others manipulate statistics to make their arguments; Trump just pulls stuff out of his ass. Evidence is irrelevant.

Further, he uses these exanus pronouncements to support his chaotic policy pronouncements, which tend to be simple-minded in the extreme. Problems are usually complex and systemic; his solutions are simple-minded and blunt as a cudgel. He shows contempt for subtlety. If the problem is illegal immigration, his solution is not to consider the cause of the immigration, but to build a wall, despite the fact that the majority of the illegal immigration does not cross the desert border, but flies into our airports. My favorite joke about the wall: “Wall — cost: $12 billion; ladder — cost: $35.”

But this is not meant to be a jab at Trump, who is clearly unhinged, not very bright, not at all subtle, and basically a bully at heart. It is too easy to target him; he is a joke. A dangerous joke, who may very well destroy the world at the push of a button, but a joke nonetheless.

No, what I want to point out is that there is, beyond Trump, a basic misunderstanding of the political divisions in the country.

The divisions are very real. Between urban and rural, between liberal and conservative, between Republican and Democrat. But I want to point out that these may overlap, like Venn diagrams, the dipoles are not identical. We too often confuse conservative with Republican and liberal with Democrat. There may be overlap, but more important, their goals are different.

There is a clear difference between liberal and conservative. As they are defined nowadays (very different from when they originated and when conservatism favored a strong central government), the conservative now seeks a smaller central government and the liberal, an activist government working for the betterment of its citizens. The one favors the individual, the other, the community. The one is exclusionary, the other inclusive. And it is clear that as the political scene is currently deployed, Republicans tend to favor conservatism and Democrats tend to the liberal, although Republicans are more extremely weighted to the far-end of conservatism than the Democrats are to the left wing.

But, such thoughts of political philosophy are largely irrelevant to the actualities of politics. One should never conflate Republican with conservative, nor Democrat with liberal. The aims of ideology are to promote a world view and an action plan to enforce that world view. But that is not the aim of the Republican party. Certainly, it will use conservative ideas to further its ends when it can, but its primary driving aim is the accrual and preservation of power. This is central and should never be forgotten: Republicans will do whatever they need to to gain and keep power. Democrats have a similar, but weaker drive. Many Democrats join the party because they think they can make the world a better place. Some Republicans do that, too, but the aim of the party on the whole is not the improvement of society, but the exercise of power. It is King of the Hill on a hemispheric playing field.

This is not to say that most Republicans don’t believe, by and large, that conservative policies would help the nation, but that whether or not they do is secondary to the accretion of political power. Hence, the contorted, serpentine Congressional districts, gerrymandered into silliness in order to ensure Republican supremacy. (Yes, Democrats have done the same — in fact, they invented the procedure in the 19th century — but they were pikers compared to the modern attempt to engineer a “permanent Republican majority.”) Hence, the bald-faced hypocrisy of choosing sides on an issue solely on the basis of whether a Republican or Democrat is offering it for a vote (as with the Republican-designed Affordable Care Act, which became an unswallowable “disaster” when recycled by the Obama administration. Hence, the use of arcane Senate or House rules, or the threat of the “nuclear option,” when it favors them, and outrage when used against them.

And it is why Republicans were gulled into supporting Trump when it looked like he might win the White House back for the party, despite the problem of Trump espousing ideas contrary to longstanding Republican policies. Trump is, after all, not a Republican, except in name, and not a conservative, as it is usually defined. He is sui generis, a propounder of Trump now, Trump tomorrow, Trump forever.

One area in which Trump and Republican world views agree is that the primary lens through which to view policy is economic. Money is the gravity that holds that world together. Whether it’s tax cuts, deregulation or fear of unions and a raise in minimum wage, the heart and soul of the conservative world view is money. The very idea of “running government like a business” is a consequence of this Weltanschauung. But across the world, this idea is changing. Governments are not businesses.

There is a historical storyline here. In the feudal past, with the king at the top of the pile, government was essentially a protection racket, with each level of vassalage “wetting its beak” in the next level down, and everyone feeding on the peasants. The general welfare of the populace was not even an empty platitude. As nation states developed from the Medieval sense of monarchal real estate, the idea of decent governance took hold. Since the New Deal in the U.S., and post-war in the better part of the rest of the world, governments have assumed the duty of protecting the welfare of its populace. All through Europe, governments guarantee health care, safety, minimum living wages, shorter work weeks and longer vacations. The U.S. has resisted such things. For Republicans (distinct from conservatives, who also have many social issues) and Trump see the world through dollar-tinted glasses. It is a reversion to the Medieval model, where all wealth floats upward like a bubble in the champagne. And it is power that guarantees the income. The goal of the Republican party is not so much the institution of conservative ideas, rather it is the use of conservative ideas to protect and increase individual wealth.

The problem is, that while money can make life easier to navigate, money cannot make life worth living. For that, you need the other aspects of life that Democrats — and most of the rest of the world — embrace. Freedom from oppression, sufficient means for living, cooperative communities, aid for the less fortunate, an even playing field for all. Among the things that make life worth living are family, love, art, religion, good health, and shared interests and shared mythology.

For Trump and the Republican party both, the world they see is transactional. It is also a zero-sum game, and the winning is all. We need to recall that when we let ourselves be gulled into arguing over conservative and liberal. Those labels are merely the masks worn in the more brutal fight over who will be the alpha dog.

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About Richard Nilsen

Richard Nilsen made a living out of having opinions. He was born in New Jersey and handed down one of his first significant opinions by leaving.

He was art critic with “The Arizona Republic” from 1986 to 2012 and is proud of the fact that he was never shot at, although he was once hanged in effigy by “Western Horseman” magazine after a nasty review of Cowboy Art. Since 2005, he was also the classical music, opera, dance, architecture and back-up movie critic.

“It’s all connected,” he says.

But newspaper work is only one of a long and eclectic series of jobs and “life positions.”

He has been a truck driver, a book packer, a sales clerk. He has taught crime-scene photography to police cadets. He has worked at the Seattle zoo and as writer and editor on a Black weekly newspaper in Greensboro, N.C. He has been both a bum and a department head in a 2-year college, where he taught photography and art history.

He once lived for a year in a coal bin.

He graduated from Guilford College in North Carolina in 1970, but when asked about his credentials, is more likely to mention the zoo.


INTRODUCTION {#s1}
============

Until 1999, drug therapy for chronic myeloid leukemia (CML) \[[@R1], [@R2]\] was limited to non-specific agents such as busulfan, hydroxyurea, cytosine arabinoside, and interferon-α (IFN-α) \[[@R3]\]. The development of small-molecule tyrosine kinase inhibitors (TKIs) has dramatically changed patients' prognosis as they potently interfere with the interaction between the *BCR-ABL1* protein and adenosine triphosphate (ATP), and block the proliferation of the malignant clone \[[@R4]\]. This "targeted" approach has significantly changed the natural history of CML and improved 10-year overall survival from less than 20% to 80-90% \[[@R2], [@R5]\]. Imatinib mesylate was the first TKI to be approved by the US Food and Drug Administration for the treatment of patients with CML-chronic phase, followed in 2007 by the second-generation TKIs dasatinib and nilotinib \[[@R6]--[@R11]\].

TKIs approved for first- and second-line treatment of CML-chronic phase have a distinct toxicity profile that includes glycometabolic alterations such as diabetes mellitus (DM), impaired fasting glucose (IFG), and the metabolic syndrome (MS), a cluster of metabolic abnormalities characterized by insulin resistance \[[@R12]\]. Based on its age distribution, it can be predicted that prevalence of CML will increase with the increasing age of the general population, and that this will lead to a significantly higher risk of developing these metabolic disorders upon treatment with certain TKIs. In fact, a large phase III trial comparing the efficacy of nilotinib and imatinib showed that hyperglycemia occurred in 50% of patients treated with nilotinib 300 mg b.i.d., 53% of those treated with nilotinib 400 mg b.i.d., and only 31% of those treated with imatinib 400 mg/day; yet, none of these patients discontinued TKI therapy because of hyperglycemia or had serious diabetes-related adverse events \[[@R13]\]. However, no data are available concerning the prevalence of DM/IFG and the MS in "real-life", unselected CML patients on TKI therapy.

Purposes of this study were (a) to assess the prevalence of glycometabolic alterations (DM/IFG, MS) in a cohort of CML-chronic phase patients on TKI therapy; and (b) to identify which parameter(s) should be evaluated at diagnosis and during treatment to help clinicians to choose the most appropriate TKI for each patient from a metabolic point of view.

RESULTS {#s2}
=======

One hundred and sixty-eight consecutive patients diagnosed as having CML-chronic phase and treated with imatinib (*n* = 92), dasatinib (*n* = 40) or nilotinib (*n* = 36) entered the study. Among them, 107 were in first-line, 53 in second-line, and the remaining 8 in third-line treatment. Moreover, only 3 patients changed TKI because of intolerance to the previous treatment, whereas all the other patients were resistant. Our cohort included 92 males (54.8%), and their median age at the time of recruitment was 56.0 years (range 21.2-87.5) (Table [1](#T1){ref-type="table"}).

###### Clinical and laboratory features of 168 CML-chronic phase patients treated with imatinib, dasatinib or nilotinib

                                                 Imatinib group (n. 92)   Dasatinib group (n. 40)   Nilotinib group (n. 36)   P-value[\*](#tfn_001){ref-type="table-fn"}
  ---------------------------------------------- ------------------------ ------------------------- ------------------------- --------------------------------------------
  Age (years), median (range)                    59.2 (21.2-87.5)         54.9 (24.5-82.6)          46.0 (25.1-79.0)          0.0044
  Male/Female, n                                 49/43                    23/17                     20/16                     0.90
  Length of treatment (months), median (range)   93.0 (0.4-165.5)         41.6 (3.0-97.1)           16.1 (0.1-70.0)           0.0001
  BMI (Kg/m^2^), mean (SD)                       25.9 (4.8)               24.8 (3.7)                26.3 (3.5)                0.24
  FPG (mg/dl), mean (SD)                         98.0 (22.8)              92.8 (12.0)               104.7 (18.9)              0.0069
  HbA~1c~ (%), mean (SD)                         5.5 (0.7)                5.5 (0.9)                 5.6 (0.6)                 0.54
  Insulin (μUI/ml), median (range)               7.9 (2.3-33.1)           6.7 (2.7-29.8)            11.0 (6.2-29.5)           0.0001
  C-peptide (μmol/L), median (range)             2.3 (1.1-8.5)            1.9 (0.9-4.2)             2.8 (1.8-4.8)             0.0003
  HOMA-IR, median (range)                        1.7 (0.6-12.0)           1.4 (0.6-7.9)             2.8 (1.4-7.0)             0.0001
  HOMA-%B, median (range)                        94.3 (22.8-550.8)        92.3 (42.1-364.1)         99.7 (34.9-393.2)         0.26
  Total cholesterol (mg/dl), median (range)      177.0 (119.0-265.0)      189.0 (114.0-337.0)       217.5 (140.0-297.0)       0.0001
  HDL cholesterol (mg/dl), median (range)        48.0 (20.0-98.0)         50.0 (23.0-109.0)         53.0 (32.0-87.0)          0.09
  Triglycerides (mg/dl), median (range)          102.0 (32.0-506.0)       111.0 (46.0-457.0)        104.0 (36.0-249.0)        0.59
  LDL cholesterol (mg/dl), mean (SD)             105.2 (32.1)             124.2 (35.4)              140.0 (36.5)              0.0001
  SBP (mmHg), median (range)                     130.0 (100.0-180.0)      122.5 (100.0-170.0)       125.0 (110.0-150.0)       0.28
  DBP (mmHg), mean (SD)                          79.0 (7.8)               77.9 (8.8)                80.1 (6.5)                0.39
  Waist circumference (cm), mean (SD)            94.3 (15.7)              90.4 (12.8)               94.2 (12.2)               0.40
  DM/IFG, n (%)                                  23 (25.0)                10 (25.0)                 12 (33.3)                 0.61
  MS, n (%)                                      39 (42.4)                15 (37.5)                 13 (36.1)                 0.77

CML= chronic myeloid leukemia; BMI= body mass index; FPG= fasting plasma glucose; SD= standard deviation; HbA1c= hemoglobin A1c; HOMA-IR= Homeostasis Model Assessment -- Insulin Resistance; HOMA-%B= Homeostasis Model Assessment -- β-cell function; SBP=systolic blood pressure; DBP= diastolic blood pressure; DM= diabetes mellitus; IFG= impaired fasting glucose; MS= metabolic syndrome.

From Kruskal-Wallis (continuous variables) or chi-squared (categorical variables) test.

In detail, at the time of assessment, patients in the imatinib group were significantly older and were receiving treatment for longer than those in the other groups. There were significant differences in fasting plasma glucose (FPG), insulin, C-peptide, and the Homeostasis Model Assessment - Insulin Resistance (HOMA-IR) index, but not in body mass index (BMI), waist circumference, hemoglobin (Hb) A~1c~ and HOMA - β-cell function (HOMA-%B). Moreover, there were significant differences in total and LDL cholesterol, but not in HDL cholesterol, triglycerides, and systolic and diastolic blood pressure.

Table [2](#T2){ref-type="table"}, [3](#T3){ref-type="table"}, and [4](#T4){ref-type="table"} show the results of the multiple regression analyses adjusted for centre, gender, age, BMI and length of treatment, and expressed as absolute differences or percent changes. FPG, insulin, C-peptide, and HOMA-IR were all significantly higher in the nilotinib group than in the other two groups, whereas there was no difference in HOMA-%B. Total, HDL, and LDL cholesterol levels did not differ significantly between the dasatinib and nilotinib groups, but they were significantly higher in both groups than in the imatinib group, except for HDL cholesterol between dasatinib- and imatinib-treated individuals. No difference in triglyceride levels was found among the three groups.

###### Multiple regression analysis adjusted for centre, gender, age, BMI, and length of treatment, comparing imatinib versus nilotinib group

                            Imatinib group (n. 92)   Nilotinib group (n. 36)
  ------------------------- ------------------------ ------------------------------------------------------ --------------------------------------------------
  FPG (mg/dl)               Reference                9.8 (1.9; 17.8)                                        0.015
  Insulin (%)               Reference                17.4 (6.2; 30.3)                                       0.002
  C-peptide (%)             Reference                11.6 (4.1; 19.7)                                       0.002
  HOMA-IR (%)               Reference                22.1 (9.4; 37.7)                                       \<0.001
  HOMA-%B (%)               Reference                2.0 (−9.5; 10.5)                                       0.76
  Total cholesterol (%)     Reference                11.6 (7.3; 16.2)                                       \<0.001
  HDL cholesterol (%)       Reference                6.2 (1.0; 11.6)                                        0.02
  Triglycerides (%)         Reference                3.6 (−5.8; 13.9)                                       0.48
  LDL cholesterol (mg/dl)   Reference                42.4 (27.0; 57.8)                                      \<0.001
                                                     **OR (95% CI)[\*\*](#tfn_003){ref-type="table-fn"}**   **P-value[\*\*](#tfn_003){ref-type="table-fn"}**
  DM/IFG                    Reference                1.7 (0.5; 5.8)                                         0.39
  MS                        Reference                0.6 (0.2; 2.4)                                         0.46

BMI= body mass index; CI= confidence interval; OR= odds ratio; FPG= fasting plasma glucose; HOMA-IR= Homeostasis Model Assessment -- Insulin Resistance; HOMA-%B= Homeostasis Model Assessment -- β-cell function; DM= diabetes mellitus; IFG= impaired fasting glucose; MS= metabolic syndrome.

From multiple linear regression models.

From multiple logistic regression models.

###### Multiple regression analysis adjusted for centre, gender, age, BMI, and length of treatment, comparing dasatinib versus nilotinib group

                            Dasatinib group (n. 40)   Nilotinib group (n. 36)
  ------------------------- ------------------------- ------------------------------------------------------ --------------------------------------------------
  FPG (mg/dl)               Reference                 9.7 (2.5; 16.9)                                        0.009
  Insulin (%)               Reference                 22.1 (11.6; 33.6)                                      \<0.001
  C-peptide (%)             Reference                 16.2 (9.4; 24.0)                                       \<0.001
  HOMA-IR (%)               Reference                 27.1 (16.2; 40.5)                                      \<0.001
  HOMA-%B (%)               Reference                 7.3 (−4.9; 20.9)                                       0.27
  Total cholesterol (%)     Reference                 3.9 (−0.8; 8.3)                                        0.10
  HDL cholesterol (%)       Reference                 3.0 (−2.0; 8.3)                                        0.24
  Triglycerides (%)         Reference                 −5.8 (−14.8; 4.1)                                      0.23
  LDL cholesterol (mg/dl)   Reference                 14.8 (−2.5; 32.1)                                      0.09
                                                      **OR (95% CI)[\*\*](#tfn_005){ref-type="table-fn"}**   **P-value[\*\*](#tfn_005){ref-type="table-fn"}**
  DM/IFG                    Reference                 1.3 (0.4; 4.1)                                         0.69
  MS                        Reference                 0.5 (0.2; 1.9)                                         0.34

BMI= body mass index; CI= confidence interval; OR= odds ratio; FPG= fasting plasma glucose; HOMA-IR= Homeostasis Model Assessment -- Insulin Resistance; HOMA-%B= Homeostasis Model Assessment -- β-cell function; DM= diabetes mellitus; IFG= impaired fasting glucose; MS= metabolic syndrome.

From multiple linear regression models.

From multiple logistic regression models.

###### Multiple regression analysis adjusted for centre, gender, age, BMI, and length of treatment, comparing imatinib versus dasatinib group

                            Imatinib group (n. 92)   Dasatinib group (n. 40)
  ------------------------- ------------------------ ------------------------------------------------------ --------------------------------------------------
  FPG (mg/dl)               Reference                0.2 (−4.9; 5.2)                                        0.95
  Insulin (%)               Reference                −3.9 (−12.2; 5.7)                                      0.42
  C-peptide (%)             Reference                −4.4 (−10.4; 2.0)                                      0.18
  HOMA-IR (%)               Reference                −3.9 (−13.1; 7.3)                                      0.48
  HOMA-%B (%)               Reference                −4.9 (−13.9; 6.2)                                      0.37
  Total cholesterol (%)     Reference                7.8 (4.1; 11.6)                                        \<0.001
  HDL cholesterol (%)       Reference                3.0 (−2.0; 8.3)                                        0.22
  Triglycerides (%)         Reference                9.4 (0.2; 20.9)                                        0.046
  LDL cholesterol (mg/dl)   Reference                27.6 (14.7; 40.5)                                      \<0.001
                                                     **OR (95% CI)[\*\*](#tfn_007){ref-type="table-fn"}**   **P-value[\*\*](#tfn_007){ref-type="table-fn"}**
  DM/IFG                    Reference                1.3 (0.4; 4.2)                                         0.60
  MS                        Reference                1.1 (0.4; 3.5)                                         0.85

BMI= body mass index; CI= confidence interval; OR= odds ratio; FPG= fasting plasma glucose; HOMA-IR= Homeostasis Model Assessment -- Insulin Resistance; HOMA-%B= Homeostasis Model Assessment -- β-cell function; DM= diabetes mellitus; IFG= impaired fasting glucose; MS= metabolic syndrome.

From multiple linear regression models.

From multiple logistic regression models.

On the basis of clinical and laboratory data, 45 patients (26.8%) were classified as having type 2 DM/IFG and 67 (41.4%) as having the MS, with no significant between-treatment group differences at both univariate (Table [1](#T1){ref-type="table"}) and multiple regression analyses (Table [2](#T2){ref-type="table"}, [3](#T3){ref-type="table"} and [4](#T4){ref-type="table"}). Only 2 patients (1.2%) discontinued TKI therapy because of the development of glycometabolic alterations, whereas 3 subjects (1.8%) required a specific treatment for hyperglycemia, without discontinuing TKI therapy.

DISCUSSION {#s3}
==========

Each of the TKIs approved for treatment of patients with CML-chronic phase has a distinct toxicity profile that should be carefully considered when prescribing TKI treatment. It is now well known that first- (imatinib) and second- (dasatinib, nilotinib) generation TKIs act on different molecular targets \[[@R14]\], and that their side effects are mainly due to the inhibition not only of *BCR-ABL1* but also of other tyrosine kinases such as *c-kit*, *PDGFR*, *Src* or *EPHB4*. Imatinib mainly causes peripheral edema, whereas dasatinib predisposes some patients to pleural effusions and, to a lesser extent, pulmonary arterial hypertension \[[@R15]\], and inhibits platelet function \[[@R16], [@R17]\]. Both drugs do not negatively impact on glucose metabolism and may even improve FPG levels and allow a reduction of the dosage of anti-hyperglycemic drugs \[[@R18]--[@R21]\]. The positive effect of imatinib might be due to an improvement of both insulin secretion, via inhibition of β-cell apoptosis \[[@R22]\] due to increased activation of antiapoptotic transcription factor *NF-kB* or decreased activation of the proapoptotic *MAPK JNK*, and insulin sensitivity in peripheral tissues, via inhibition of tumour necrosis factor-α production \[[@R23]\] and/or endoplasmic reticulum stress \[[@R24], [@R25]\]. Conversely, nilotinib was shown to induce hyperglycemia in a subset of non-diabetic CML patients \[[@R13], [@R26], [@R27]\]. The underlying mechanism is controversial as a previous analysis of 10 patients demonstrated a reduction in insulin sensitivity \[[@R28]\], whereas a case report showed a reversible decrease in insulin secretion \[[@R29]\] under nilotinib therapy. Regarding lipid profile, total, HDL, and LDL cholesterol levels were found to increase significantly in patients treated with nilotinib, and to decrease in those receiving imatinib \[[@R30]\]. The latter effect might be due to the fact that imatinib can act also on *PDGFR*, which may induce phosphorylation of LDL receptor-related protein \[[@R31]\].

Our cross-sectional analysis assessed the prevalence of DM/IFG and the MS in a large series of "real-life", unselected CML-chronic phase patients during TKI treatment and estimated both insulin sensitivity and β-cell function in these individuals. We found a non-significant trend toward an increase in the prevalence of DM/IFG, but not the MS, in CML-chronic phase patients treated with nilotinib *vs* those receiving imatinib or dasatinib. Moreover, individuals on nilotinib showed significantly higher FPG, insulin, C-peptide, and total and LDL cholesterol levels and HOMA-IR values, with no differences in HbA~1c~ and HOMA-%B.

The increase in FPG, but not in HbA~1c~ levels, in patients treated with nilotinib suggests that the hyperglycemic effect of this drug may not be clinically meaningful and that it likely does not affect post-prandial glucose concentrations. This interpretation is consistent with the higher HOMA-IR, but not HOMA-%B values, indicating an impairment of insulin sensitivity with no effect on β-cell function. Also the lack of significant increases in the prevalence of DM/IFG observed in patients receiving nilotinib, which is at variance with a previous report from a phase III trial \[[@R13]\], seems to argue against the concept that the effect of this drug on glucose metabolism represents a real clinical problem. This view is consistent with a previous report that fasting hyperglycemia does not progress to type 2 DM in nilotinib-treated individuals \[[@R27]\]. However, our prevalence data might have been influenced by a selection bias caused by the significant inter-groups differences in age and duration of TKI treatment, which may have not been accounted for by statistical adjustment. In fact, patients on nilotinib were younger and with a shorter treatment duration than those on imatinib and dasatinib. Taken together, our data, while excluding a potent diabetogenic effect of nilotinib, do support the need for a close monitoring of glucose (and lipid) metabolism in patients receiving this drug and for a multidisciplinary approach to manage rises of FPG (and cholesterol) levels during treatment. In clinical practice, it would be appropriate to measure FPG, cholesterol, and possibly HbA~1c~ levels before treatment to identify subjects at risk to develop glycometabolic abnormalities and to guide the choice of the TKI. Moreover, the same parameters should be assessed at 3 months of treatment, and in the nilotinib group every 6-12 months thereafter. This strategy would be adequate for reducing cardiovascular burden associated with hyperglycemia and dyslipidemia, which may increase the risk of atherosclerosis with peripheral artery occlusion occurring with nilotinib treatment \[[@R30], [@R32], [@R33]\].

Strengths of our study include the "real-life" setting and the size of the cohort, which is much larger than in previous reports \[[@R18]--[@R21], [@R26]\]. Limitations include the cross-sectional design of the study, the already mentioned differences among treatment groups in terms of age and duration of TKI therapy, and the criteria for excluding the presence of DM and the MS at baseline. In addition, the fact that an oral glucose tolerance test was not performed in the study subjects might have resulted in an underestimation of the prevalence of DM.

In conclusion, this cross-sectional analysis of CML-chronic phase patients on treatment with three TKIs showed that prevalence of DM/IFG or the MS was not significantly different, though glycometabolic profile was worse in subjects treated with nilotinib than in those on imatinib or dasatinib. These findings indicate the need for a close monitoring of glucose and lipid metabolism and a multidisciplinary approach in patients treated with nilotinib.

MATERIALS AND METHODS {#s4}
=====================

Patients {#s4_1}
--------

One hundred and sixty-eight consecutive CML-chronic phase patients attending the Oncohematology Division of the IRCCS Ca' Granda -- Maggiore Policlinico Hospital Foundation, Milan, and the Hematology Unit of the IRCCS Policlinico San Matteo Hospital Foundation, Pavia, were recruited between May 2013 and July 2014. The study was approved by the local Ethics Committees, and all patients gave their written informed consent in accordance with the Italian guidelines for the protection of human subjects. Inclusion criteria were age \>18 years, CML-chronic phase diagnosis based on World Health Organization 2008 criteria, and current treatment with imatinib, dasatinib or nilotinib \[[@R14]\]. CML patients who received only IFN-α therapy were excluded from the analysis, as were those with a previous history of DM/IFG, MS, or statin treatment. FPG levels and body weights before starting TKI treatment were also available in all cases.

Methods {#s4_2}
-------

Anthropometric and metabolic parameters of each patient were assessed at enrollment. All patients underwent physical examination, with assessment of BMI, waist circumference, and blood pressure, and biochemical testing, including HbA~1c~, FPG, insulin, and C-peptide levels, and serum lipid profile.

As a surrogate measure of insulin secretion, the HOMA-%B was calculated from fasting insulin and glucose concentrations by the formula: (20xIns~0~)/(Gluc~0~-3.5). The HOMA-IR index was estimated using the formulas proposed by Levy *et al*. \[[@R34], [@R35]\].

Diabetes mellitus, IFG, and the MS were diagnosed according to the criteria of the American Diabetes Association, updated in 2010 (based on FPG and HbA~1c~ levels) \[[@R36]\], and those of the National Cholesterol Education Program - Adult Treatment Panel III \[[@R12]\].

Statistical analysis {#s4_3}
--------------------

The Kruskal-Wallis and chi-squared tests were used to compare continuous and categorical variables, respectively, in the treatment groups. The laboratory results were evaluated by fitting multiple linear regression models adjusted for centre, gender, age, BMI, and length of treatment. Two sets of comparisons were made: 1) nilotinib and dasatinib *vs* imatinib; and 2) nilotinib *vs* dasatinib. In the case of FPG and LDL cholesterol, the regression coefficients represent absolute differences (in mg/dL). Conversely, as the other laboratory data were right skewed, we fitted regression models using their log~10~-transformed values as dependent variables, and then calculated their percent changes using the formula: % change = \[antilog~10~(Coefficient) -- 1\]×100 \[[@R37]\]. For DM/IFG and the MS, we used logistic regression models to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs), once again using the imatinib or dasatinib groups as reference \[[@R38]\]. The statistical analyses were made using Stata 13 software \[[@R39]\].

This work was supported in part by grant from Fondazione Alberto e Antonietta Matarelli and AIL - Associazione Italiana contro le Leucemie, Linfomi e Mieloma -- Milano e Provincia ONLUS.

**FUNDING**

This work was supported by funds from the authors' institutions.

**CONFLICTS OF INTERESTS**

AI received a speaker funding from Bristol Myers Squibb, NOVARTIS, ARIAD, Pfizer and SHIRE. EO has been a member of advisory boards or a speaker with Eli Lilly, Boehringer Ingelheim, AstraZeneca, Roche, MerckSharp & Dome and Lifescan J&J. EMO received a speaker funding from Bristol Myers Squibb, NOVARTIS and Roche. DCa, VR, CB, NO, MS, CE, VG, DCo and AC declare that they have no conflict of interest.

**Author contributions**

AI was responsible for the integrity of the work as a whole

AI and EO designed the study

AI, EO, DCa, VR and EMO interpreted the data and wrote the manuscript

DCo made statistical analysis

AI, EMO, DCa, CB, NO, MS, CE and VG followed the patients and collected the data

AC reviewed the data and approved the manuscript


Fig. A1. Timing of measurement of reproductive traits (in green) during the first year of life of Alpine marmots (wide gray arrow). Each life event is illustrated by a gray box. Pre-weaning survival is represented by a red curly bracket. The period of hibernation is illustrated by a blue strip.


Q:

Json data truncated on cell network ok on wifi

Using the following code to retrieve data via a php file from MySQL works great on wifi but not on mobile it seems to truncate the response. Any help to approach debugging this would be much appreciated. See print outputs pasted into code below for cell v wifi
func get(){

let urlcustom = "http://myserver.com/swiftly/phpget.php?team_id=\(theteam)"
let url = URL(string: urlcustom)
let data = try? Data(contentsOf: url!)
values = try! JSONSerialization.jsonObject(with: data!, options: JSONSerialization.ReadingOptions.mutableContainers) as! NSArray
tableView.reloadData()
print("get ran \(data)")
// mobile phone cell connection - FAIL
// get ran Optional(<5b5d>)

// Wifi Connection
//get ran Optional(<5b7b2269 64223a22 31222c22 7465616d 5f696422 3a223122 2c226e61 6d65223a 22457669 65205072 6f637465 72222c22 646f6222 3a223230 30362d30 312d3033 222c2270 686f746f 223a2222 7d5d>)

}

A:

I added this to all my files :
<?php
header("Content-Type: application/json"); header("Expires: 0");
header("Last-Modified: " . gmdate("D, d M Y H:i:s") . " GMT");
header("Cache-Control: no-store, no-cache, must-revalidate");
header("Cache-Control: post-check=0, pre-check=0", false);
header("Pragma: no-cache");
$host='localhost';

All works now.


Our summer gift collection is here! And yes, its beach themed! We have silver and deep blue nautical theme candle holders in staggered sizes. Some beach glass vases from full fabulous size to bedside bud vase. And my personal favorite, a set of 4 drinking glasses with aqua octopi. Oh!... and of course the blue crab box of matches.

Come say hello and grab a royal blue octopus votive. There is certainly someone you know who needs this.


That's something they wouldn't have had to do before last year. But then, the warrants aren't much of a safeguard, because we don't know whether they were asked for or granted. We're not allowed to know. In the Kafkaesque world the politicians of both major parties have allowed Australia to become, anyone who tells us can go to prison for two years. But the real problem with the raids is worse. Though no one seems to have noticed it, the AFP is trying to use very old laws to pursue not just the suspected leaker, but the recipients of the leaked documents. A redacted version of the search warrant that authorised the police raids in Melbourne was posted over a week ago by the ABC's Media Watch program. Yet, so far as I'm aware, no one has pointed out what a truly alarming document it is. The warrant justifies the searches on the grounds that two separate offences might have been committed. The first is that "in an as yet unidentified place an as yet unidentified Commonwealth officer communicated documents relating to the National Broadband Network to [redacted name] contrary to subsection 70(1) of the Crimes Act". Now section 70 is short, and notorious: it makes it an offence, punishable by two years' imprisonment, for a "Commonwealth officer" to communicate any information that he or she comes by in the course of their duties to anyone who is not authorised to receive it.

According to the Crimes Act, a "Commonwealth officer" includes "for the purposes of section 70, a person who, although not … employed by the Commonwealth … performs services for or on behalf of the Commonwealth". And that, at least according to the AFP's lawyers, includes an employee of the NBN Co. So, in our brave new world, someone proven to have leaked a document about how the nation's most expensive infrastructure rollout is being managed, regardless of whether the leak is or isn't in the public interest (for no such test applies), can be thrown into the slammer for two years. But it gets much worse. Because the second offence the AFP was investigating, according to the search warrant, is that "[redacted name] did receive documents knowing at the time he was not authorised to receive the documents, contrary to section 79(6) of the Crimes Act". The suspected criminal in this case is not the leaker, but the recipient of the leak. Section 79 is headed "official secrets". It is concerned primarily with preventing the unauthorised disclosure to third parties of information relating to defence and national security. But its phrasing is broad. It states that a "prescribed document" is one that a Commonwealth officer obtains and which "by reason of its nature … or for any other reason, it is his or her duty to treat as secret".

Now, in law, there's a difference between having an obligation to keep something confidential, and having a duty to keep it secret. "Secret" is a heavy word. Yet the AFP search warrant claims that background briefing documents about the rollout of the NBN are "secret", and that as a consequence anyone who can be proved to have received them, knowing that they weren't authorised to do so, can be thrown into the slammer for two years, just like their source. This is extraordinary, and to my knowledge, unprecedented. We now have a federal police force that believes that journalists or (in this case) political staffers who are leaked documents by an employee of a Government Business Enterprise may be committing an offence – no matter what the public interest in disclosure may be, no matter what the documents' contents, and no matter whether or not the recipient staffer or journalist discloses them to anyone else. As one of the lawyers I consulted wrote to me: "I have never heard of anything so crazy … It's ridiculous they got a search warrant partly on that basis." But they did. These days, it seems, the police and security forces can get just about anything they want, even when the issue has nothing to do with national security. I said a year ago in this space that Australia is on the way to creating a secret police. This, I'm told, is to be my last regular column for Fairfax Media. But here, or elsewhere, I'll try to keep tabs on our ever-diminishing freedoms.

We all should.


Enantiomer separation of drugs by capillary electrophoresis using proteins as chiral selectors.
The separation of drug enantiomers using proteins as the chiral selectors in capillary electrophoresis (CE) is considered in this review. The proteins used include albumins such as bovine serum albumin, human serum albumin and serum albumins from other species, glycoproteins such as alpha1-acid glycoprotein, crude ovomucoid, ovoglycoprotein, avidin and riboflavin binding protein, enzymes such as fungal cellulase, cellobiohydrolase I, pepsin and lysozyme and other proteins such as casein, human serum transferrin and ovotransferrin. Protein-based CE is carried out in two modes: in one proteins are immobilized or adsorbed within the capillary, or protein-immobilized silica gels are packed into the capillary (affinity capillary electrochromatography mode), and in the other proteins are dissolved in the running buffer (affinity CE mode). Furthermore, the advantages and limitations of the two modes and the factors affecting the chiral separations of various drugs by protein-based CE are discussed.


Tuition Is Up -- As Is Uncertainty

By

Both tuition and financial aid are up for the current academic year -- even as the economic uncertainty leaves many colleges and students with worries about next year’s charges.

The average tuition for a private four-year college topped $25,000 for the first time in 2008-9, hitting $25,143, or 5.9 percent more than last year’s total, according to the latest annual report by the College Board, released on Wednesday. At public, four-year colleges, tuition rates for in-state residents were up 6.4 percent, reaching $6,585. College Board officials noted that with inflation for the year running at 5.6 percent, these increases were only modestly above the inflation rate. In some recent years, when the tuition increase averages have further exceeded the inflation rate, politicians and others have questioned why costs were going up so rapidly.

Sandy Baum, a senior policy analyst for the College Board, said that “in nominal terms,” the increases aren’t that different from what they have been in recent years. But there may be a difference in the public perception because “the inflation rate turned out to be so high this year,” she said. At the same time, she acknowledged, while this may lessen Congressional or other criticism, it may provide little comfort to families. “This doesn’t mean it’s easier for families to pay, because all of their other prices went up, too,” she said.

For some sectors, tuition rates this year went up by less than the rate of inflation.

The average tuition for out-of-state students at public four-year colleges this year is $17,452, up 5.2 percent. Community colleges’ average tuition reached $2,402, up 4.7 percent. And in for-profit higher education, the average is now $13,046, up by the smallest percentage of any sector -- 4.5 percent.

Students who live in campus housing are paying about $8,000 a year for room and board, or equivalent costs for their own housing and food, the College Board found. Those prices are up around 4 percent, across sectors. These totals for tuition and room and board leave out other expenses faced by students, including textbooks and transportation.

If this year’s figures largely follow the pattern of recent years of being slightly above or below the rate of inflation, next year could be different. While the economy has generally worsened during the last year, the shocks of the credit crunch and the huge losses on Wall Street took place after colleges made the decisions that led to this year’s totals.

Many states are reporting massive deficits and numerous governors and state budget offices have warned that next year’s legislative sessions will be among the most challenging in many years, in terms of balancing budgets. Economic downturns -- even some of lesser magnitude than what the United States may now be experiencing -- have in years past seen states dictate double-digit tuition increases at many public institutions.

While many private colleges are also extremely anxious about the economic downturn, Baum predicted that they may respond in different ways, at least with regard to tuition rates. Private colleges have for years noted -- and the College Board’s data back them up -- that relatively few students pay the full listed price, given the array of aid and loans available from federal, state and institutional sources.

As the economy has soured this fall, however, some reports have suggested that private colleges may be losing students due to lack of funds, and many private college presidents have been saying privately that they fear much more this year than in the past that sticker price could scare off families. Baum said that such logic could result in private colleges finding ways to limit increases next year. “They are going to be intensely aware that people don’t have much money to pay,” she said.

Still, for tuition-dependent institutions -- which many private colleges without large endowments are -- keeping tuition down may make it more difficult to meet increased demand for financial aid. Said Baum: “The tension may be: Are they going to decide they need more money for financial aid or to just hold tuition down?”

Already, some private colleges are announcing their tuition plans for next year earlier than in the past and with smaller increases than in the past. Augustana College, early this month, announced the smallest percentage increase in tuition in 25 years, a decision made much earlier than normal in an attempt to reassure students and their families. Benedictine University announced this month that it would freeze tuition at current levels for all currently enrolled students through spring of 2010, while telling those applying to next year’s freshman class that they will not see a tuition increase until the spring of 2011.

A major theme of the College Board report, year after year, is that the most expensive colleges and universities (the ones that get the most press coverage) are not typical of higher education as a whole, in terms of what students are charged. This year’s report noted that only 19 percent of full-time undergraduates at four-year colleges and universities are enrolled at institutions where the tuition charge is at least $24,000. In contrast, 29 percent attend institutions that charge less than $6,000. (And those figures, for both groups of students, exclude the aid students receive.)

Until this year, the College Board has released data combining four-year colleges and universities in public and private sectors, but not differentiating further. Noting that there are real differences among sectors -- with doctoral institutions more expensive -- the College Board this year released breakdowns in greater detail than in the past. (The figures below for public institutions are in-state residents only.)

In 2007-8, undergraduates received an average of $8,896 in financial aid, including $4,656 in grants and $3,650 in federal loans.

The number of Pell Grant recipients increased by 5 percent, to 5.4 million in 2007-8, following two years in which the total held steady at 5.2 million. This data predates reports this year of sharp increases in the number of students applying for federal aid.

Private and public institutions continue to provide large sums of institutional financial aid, with its purpose a mix of helping students with financial aid and using merit awards to attract students who may not have financial need. Data for these figures come from 2006-7, when private four-year colleges awarded almost $7,500 per student, 70 percent of that to help meet financial needs. For public four-year colleges, the figure was more than $1,000 per student, with about 44 percent for financial needs.

One of the trends that the College Board has documented -- with some concern -- in recent years has been the rapid increase in private loan volume. These loans are frequently at higher interest rates and are almost always with fewer protections than students receive in federal loan programs -- but many students and families are not aware of the differences.

Between 2006-7 and 2007-8, private loan volume declined slightly, from $19.2 billion to $19.1 billion. This follows a decade in which such loan volume increased by about 23 percent a year.

Baum said it is unclear why the private loan volume started to fall. These figures predate the credit crunch this year that may be affecting some students seeking private loans. But Baum noted that the percentage increases in private loan volume have been so large for so long that it was unlikely that volume could have continued to increase at that rate.


Every day, the job of President-Elect seems to become more danger-filled for Donald Trump. It can honestly be said that it looks like the entire establishment is out to destroy his presidency before it even gets off the ground. As we have said for months, all signs are pointing to Hillary Clinton working behind the scenes to further this coup.

Today’s revelation is nothing less than a bombshell in the swamp that is our U.S. Government in Washington; with the curtains pulled back, it’s now clear precisely what has happened. Tragically, new evidence has emerged that shows that despite their public differences, both parties are largely different shades of the same corrupt mud.

New information from Wikileaks is about to shake up the political world. Those in question are “Republicans in name only” or “RINOs.” An email from John Podesta to Huma Abedin that was released as document number 1078645 is about to turn the speculation that certain prominent Republicans who opposed Donald Trump into the truth that they were, in fact, not just disloyal to Trump and the party but were on Hillary Clinton’s payroll.

The email, sent in July of this year, describes how funds were being diverted from Clinton’s campaign to the Super PACS of Jeb Bush, Carly Fiorina, and John Kasich.

According to the email:

“JB, CF, and JK PACS will be noticeably silent for the rest of the campaign. Each will receive a significant allowance from advertising budget. HRC is in the loop and has talked to all three personally. Eyes only.”

Other emails that surfaced but do not refer to anything other than title have also surfaced that raised eyebrowS. It seems at a glance that the Clinton Foundation, or as I am calling it, the Pantsuit Mafia, has bought off several key members of the Republican Party to push the Clinton agenda. Such as:

“He is on board, will retract the invitation to speak. Eyes only.”

This email was dated days before Speaker of the House Paul Ryan withdrew the invitation to Donald Trump to speak at an event in Wisconsin. Even though we do not have the ‘smoking gun’ to say it was him, no other logical conclusion can be assumed.

Other emails hint at the money being moved to Republican elected officials in the House and Senate. For instance FEC reports shows that two large donations from PACS and private sources ln early October went to John McCain right after he attacked Trump publicly criticized Trump. That happened shortly after a slew of emails concerning moving money to support one candidate and move support from another.

Shortly thereafter, his challenger in this tight race, Kirkpatrick, lost several key donors and money and support lessened from the DNC and the DSCC in the last few weeks of the race. The thing to note is that McCain is one of the lead sponsors of a committee to investigate any Russian influence into the election.

Senator Lindsay Graham, another outspoken critic of Donald Trump and briefly candidate for President from July to December also it seems received help from the Clintons. An email that simply states, “Cleared the road for him in 2020,” could mean that there will be no strong or supported Democrat in the South Carolina Senate race when Graham is up for re-election.

As with McCain, Graham has publicly called for a look into the Russian influence in the election.

There were a lot of politicians who were opposed to Donald Trump. These, in particular, all share a common bond, however: Trump humiliated them on stage in front of hundreds of millions of people around the world. This is more than just politics or conscientious objecting, this was revenge.

Not just revenge. This is out and out treason. Our representatives took an oath to support our Constitution and its laws for our benefit, not theirs. This is why gridlock is prevalent in D.C.–the treachery and the corruption are on both sides of the coin.

Unfortunately, there is no way of knowing who the true Republicans are and who are the ones that are fronts for the Clinton machine, the Pantsuit Mafia. The number of emails is too overwhelming to easily sift through them all to find all of the turncoat RINOs. We must be diligent to ferret out those that have sold their souls for power.

The only hope that we have for transparency of the Clintons anytime soon rests in an audit carried out by the IRS.

Behold, in recent days, there is a call to impeach the Commissioner of the IRS. Interesting times we live in to be sure. Only time and work on our part will push this audit forward.

Even though Hillary lost the Presidency, she is still a power behind the scenes with these revelations. With her Democratic allies in Pelosi and Schumer and her Republican lackeys in the form of Ryan and other Republicans, she will set the agenda and pull the strings. We the people will have no idea who is on our side and who is not.

For the Clintons, it will mean that even though the American people are supporting Trump and making America great again, they will set the agenda. We will be left scratching our heads and wondering how the GOP and Trump cannot accomplish anything. Clinton will be in control without having to ever show her hand or be responsible to the people.

The Soros-Clinton team will have killed the Republic and we will never know until it is too late.

This Russian news story perhaps is the ultimate coup for the Clinton Camp. She does not even have to be the winner of the Electoral College vote to have gained power. Should enough electors defect to Hillary or name another candidate in their votes (Electors are NOT legally bound to vote for their candidate), then that could throw the vote into the House and Senate.

The suspicious part of this simply is this, On January 20th a new president will be inaugurated. The Constitution in the 25th Amendment spells out the line of succession if there is still not a picked President, the Vice President shall become president (Kaine or Pence).

If the Senate has not picked a Vice-President by noon on the 20th, the Speaker of the House shall become President. Coincidence?

The Roman Republic lasted for centuries in the hands of the people before falling to become the Roman Empire. That change did not happen by a foreign invasion or foreign intrigue. No barbarian or forceful enemy defeated that Republic over 2000 years ago.

It was defeated from the inside by the treachery of the Senate and the blood of the slain Julius Caesar. Just as Caesar was stabbed in the back by men he thought were his allies, Trump is facing the same treachery.

In the coming days and months, without our help and diligence, President Trump may also be gasping…..”Et Tu, Paul?”


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