What Is a Stroke? Causes, Symptoms, and Treatments

Knowing the signs of a stroke is the first step in stroke prevention. A stroke, sometimes called a “brain attack,” occurs when blood flow to an area in the brain is cut off. The brain cells, deprived of the oxygen and glucose needed to survive, die. If a stroke is not caught early, permanent brain damage or death can result.

How Does a Stroke Occur?

There are two types of stroke.

• Ischemic stroke is similar to a heart attack, except it occurs in the blood vessels of the brain. Clots can form in the brain’s blood vessels, in blood vessels leading to the brain, or even in blood vessels elsewhere in the body and then travel to the brain. These clots block blood flow to the brain’s cells. Ischemic stroke can also occur when too much plaque (fatty deposits and cholesterol) clogs the brain’s blood vessels. About 80% of all strokes are ischemic.
• Hemorrhagic (heh-more-raj-ik) strokes occur when a blood vessel in the brain breaks or ruptures. The result is blood seeping into the brain tissue, causing damage to brain cells. The most common causes of hemorrhagic stroke are high blood pressure and brain aneurysms. An aneurysm is a weakness or thinness in the blood vessel wall.

What Are the Symptoms of Stroke?

The most common symptoms of a stroke are:

• Weakness or numbness of the face, arm, or leg on one side of the body
• Loss of vision or dimming (like a curtain falling) in one or both eyes
• Loss of speech, difficulty talking, or understanding what others are saying
• Sudden, severe headache with no known cause
• Loss of balance or unstable walking, usually combined with another symptom

What Should I Do If I Experience Stroke Symptoms?

Immediately call 911 if you or someone you know has symptoms of a stroke. Stroke is a medical emergency. Immediate treatment can save your life or increase your chances of a full recovery.

Is it Possible to Prevent a Stroke?

Up to 50% of all strokes are preventable. Many risk factors can be controlled before they cause problems.

Controllable Risk Factors for Stroke:

Uncontrollable Risk Factors for Stroke:

• Age (>65)
• Gender (Men have more strokes, but women have deadlier strokes)
• Race (African-Americans are at increased risk)
• Family history of stroke

Your doctor can evaluate your risk for stroke and help you control your risk factors. Sometimes, people experience warning signs before a stroke occurs.

These are called transient ischemic attacks (also called TIA or “mini-stroke”) and are short, brief episodes of the stroke symptoms listed above. Some people have no symptoms warning them prior to a stroke or symptoms are so mild they are not noticeable. Regular check-ups are important in catching problems before they become serious. Report any symptoms or risk factors to your doctor.

Bloomington Drosophila Stock Center: Indiana University Bloomington

An increasing number of fly lines are being developed which faithfully model various aspects of human disease and human health-related processes. Listed on these pages (see links below) are fly stocks available at Bloomington which can be used to study a variety of human diseases as well as human health-related subjects such as metabolism.

In addition to characterized fly models of human disease, we may also list putative orthologues for disease-associated genes not yet studied in flies, as well as stocks for studying associated genes and pathways. We identify orthologues using a variety of tools, including Homologene and DIOPT, the literature and our own BLAST searches. You should confirm orthology using your own favorite method. Be advised that inferring function from sequence/phylogeny-based orthology is often, but not always, a valid assumption.

IMPORTANT – In some instances, not every available stock for a particular gene is listed. See here for a list of our inclusion/exclusion criteria. Researchers looking for additional tools (for instance, insertions from which to make deletions) should look at the full list of available lines on the relevant FlyBase gene page.

These pages are a work in progress and we welcome any and all input. If you see any errors, notice missing genes or stocks, have suggestions on additional diseases to include, or have human disease-related stocks you would like to make available at the BDSC, please contact us.

Download a csv or Excel (xlsx) file containing all the disease gene stocks found on these pages with a column that includes the full genotype of the stock.

Other resources:

• FlyBase Human Disease model page – links to FlyBase human disease reports with information on symptomology, causative genes, molecular mechanisms, Drosophila orthologues and models, references, and much more
• DRSC Disease Gene Query (DIOPT-DIST) – a query tool for associating human disease and related human genes with orthologues in fly, mouse, C. elegans, yeast and zebrafish; search using a fly gene or a human disease or disease term and the query returns a table that includes disease, associated human gene and the fly orthologue
• Genes conserved between human and fly – precomputed tables based on Ensembl Biomart and FlyBase
• Drosophila Models of Human Disease – a blog for fly models of human disease with a focus on reporting new key papers, new resources (e. g. fly stocks) and bioinformatics tools and databases
• Homophila – a database of OMIM-derived human disease genes and their fly homologs
• Interactive Fly’s human disease page – a list of diseases studied in flies with links to papers
• Tennessen metabolism gene list – a list of genes predicted to function in metabolic pathways from Jason Tennessen.

How are the Stock Market, Earthquakes, and Disease Related? | by Agni Kumar

We model infection spread using Hawkes processes to learn various characteristics of infection influence. Specifically, we fit Hawkes processes to event sequences at the unit level, where each new infection increases the rate of future infection onsets for some period of time.

We design and implement a model to capture the dynamics of multiple event sequences jointly and also systematically uncover clustering structure among sequences. To improve our estimations of unit infectivities, intrinsic intensities, and decay rates (i. e., the model parameters), we incorporate prior knowledge about influence patterns into our learning framework. Our experimental results show that doing so improves parameter estimation as compared to multiple baseline methods. We also integrate the ability to learn an arbitrary triggering kernel, as current state-of-the-art approaches tend to assume specific kernel functions, e.g., exponential decay.

Data visualizations

The data used in this work consists of timestamps of CDI-positive diagnoses along with unit labels. An event is defined as the date on which a patient’s CDI test resulted in a positive diagnosis, along with an indication of the unit in which the patient resided at the time of the diagnosis. In the table below, we provide details about the hospital’s units and their room compositions.

Anonymized unit acronyms and characteristics

In the following plot, we visualize the distributions of events by unit, in descending frequency order. Unit labels incorporate their types, one of GMU, ICU, OU, MU, or EMD. We note that event counts differ greatly across units, but that events tend to cluster in interesting ways regardless of event count.

Frequencies of events by unitDistributions of event timestamps by unit

We propose a model to learn both the triggering patterns of each event sequence and the structures across various sequences together, where each event sequence includes multiple types of events. The model parameters fall into two groups: (1) a global triggering kernel g shared by all the sequences, which reflects influential effects from past events, and (2) a local infectivity matrix and local intrinsic intensity vector, both associated specifically with sequence a sequence of positive CDI events associated with each unit.

For a system in which U is the number of sequences and C the number of event types, we denote by A, measuring C² × U, the flattened infectivity matrix obtained by concatenating U individual C × C infectivity matrices. We let μ, measuring C × U, the intrinsic intensity matrix formed by concatenating U individual 1 × C vectors.

Structural regularization

To enable pattern learning across sequences, sparse and low-rank constraints are imposed on A. The sparsity constraint encompasses the idea that typically within each sequence, only a subset of event types occurs and triggers others. The intuition behind the low-rank constraint is to derive clustering structures when the overall triggering patterns of multiple sequences are similar and thus yield similar unit infectivity values. We also impose a low-rank constraint on μ, as we hypothesize that intrinsic intensity values could be similar across several units. These constraints enable us to incorporate prior knowledge about infection influence.

Optimization problem

We learn the Hawkes process model with maximum likelihood estimation. The parameters A, μ, and g can be learned by solving the following optimization problem:

where the first term denotes the negative log-likelihood of the model and the second term regularizes the triggering kernel to ensure it is smooth. The parameters λ₁, λ₂, and λ₃ control the strengths of the low-rank regularization on A, the sparsity regularization on A, and the the low-rank regularization on μ, respectively. The negative log-likelihood can be expanded as:

Above, u denotes a particular unit, c denotes a particular event type, and a is an element of A. nᵤ is the number of events in and Tᵤ is the greatest timestamp of the sequence associated with u. Recall that λ is the intensity function, taking the form:

An alternating direction method of multipliers (ADMM) scheme with a majorization-minimization step at each iteration is applied to solve the optimization problem. The outputs are learned values of A, μ, and g. The learning algorithm and parameter updates are detailed here, along with a robustness analysis. The code is linked here.

Inference results at the unit level

To characterize each unit’s CDI influence patterns, we fit a Hawkes process to the hospital as a whole, inputting 20 sequences, one for each unit with more than 20 events, in which all event labels are same, corresponding to positive CDI diagnoses (U = 20 and C = 1). We train a model on the first half of the data in time. We then test the model on the remainder of the data. We obtain a learned (Aᵘ, μᵘ) pair for each unit u and a single triggering kernel associated with all units.

These intensity and infectivity values are plotted, with one unit of each type labeled. We interpret the learned infectivity values as measures of the degree to which CDI events in the unit u trigger further CDI events in u. The intrinsic intensity values describe the arrival of events triggered by external sources.

Learned unit intensity and infectivity values

A key results is the EMD exhibiting the highest infectivity of all the units. That the emergency department is a key player in spreading CDI is not surprising, as it is a location where proper cleaning and disinfecting can become difficult given a constant influx of patients and high patient turnover. Moreover, issues with overcrowding make isolating patients tough.

We also see that general medical units tend to differ slightly in terms of room layout, so it is understandable that, overall, they exhibit varied infectivity and intrinsic intensity values. A closer look reveals that the GMUs that have high infectivites also have high intrinsic intensities. The low A values are likely reflections of good isolation practices, where patients are treated in private rooms after they begin showing symptoms of CDI. Varying μ values could be explained by GMUs providing care to patients of various demographics with differing underlying disease profiles.

Units with private rooms are expected to have low infectivity values, as physical divides decrease the transmission of spores. Indeed, we see that all oncology units and most ICUs shown have low infectivity values, indicative of good isolation practices.

The model identifies that MUs exhibit relatively moderately high A values perhaps because several CDI patients are routinely moved to these units, suggesting that the isolation and cleaning protocols of these units be revised. Points positioned in especially unexpected locations, e.g. ICU-1, were reported as such to the hospital.

Inference results at the hospital level

At the hospital level, we seek to generate infectivity values for all pairs of units. We do so by fitting a Hawkes process to a single sequence with all CDI events in the dataset, with a label denoting the unit with which an event is associated (U = 1 and C = 20). As before, we train the model on the first half of the data in time and test on the second half.

We achieve the hospital-level infectivity matrix given below, where each cell measures the infectivity of type-u’ events to type-u events for the receiver unit u and the influencer unit u’. Visually, the cell values correspond to how much the units on the horizontal axis influence CDI events in the units on the vertical axis. We see that the infectivities when u = u’ tend to be the highest, since patients are more likely to become infected due to the unit environments in which they reside than because of effects from other units. As shown, we cluster the influencers on the horizontal axis by the distributions of their infectivites across units. We group the receivers on the vertical axis by the distributions of the CDI influence effects they receive from other units.

Learned hospital infectivity matrix

From the matrix, we identify the top five influencers as the EMD, MU-1, ICU-1, GMU-2, and GMU-6. This aligns with the results presented in the previous section, i.e., that these units were most influential in triggering future events linked to themselves.

We identify the top five receivers as EMD, ICU-1, GMU-6, GMU-2, and OU-3. That several other units influence CDI cases in the EMD is reasonable, given the high activity it incurs from frequent patient movement. That ICU-1, GMU-6, and GMU-2 are top receivers, but that the CDI influence effects they experience are primarily due to themselves, is useful to note in terms of checking their isolation protocols. While we expect OU-3 to be labeled as a low influencer due to its self-infectivity value, it is surprising that is also a high receiver. The infectivity matrix indicates that OU-3 experiences moderate CDI influence effects from several GMUs, which is noteworthy and motivates its reporting to the hospital for further investigation.

Performance comparisons

To evaluate the relative performances of the unit-level and hospital-level models, we plot various test log-likelihood values as the training set size increases. We run each experiment ten times, plotting the average result with single standard deviation error bars.

Farm Animals | Healthy Pets, Healthy People

Influenza, or “flu”, is a contagious respiratory illness caused by influenza viruses. Some influenza viruses that primarily circulate in animals have on rare occasion infected people. For example, some avian influenza (bird flu) viruses as well some swine influenza viruses can cause infections in people. When influenza viruses that normally circulate in animals cause an infection in people, this is called a “novel” virus infection. Not all influenza viruses found in birds or pigs are known to cause human infections.

How it spreads: Flu viruses are highly contagious. Most experts think that flu viruses spread mainly by droplets made when people with the flu cough, sneeze, or talk. These droplets can land in the mouths or noses of people who are nearby or possibly be inhaled into the lungs. People can also spread flu to each other. Avian pdf icon[PDF – 2 pages] and swine pdf icon[PDF – 2 pages] flu can spread to people through contact with the virus from saliva, nasal secretions, or poop from infected animals. A person can also get flu through contact with virus-contaminated surfaces, poultry coops, pig pens, and supplies. Less often, flu can also spread to people through touching infected animals and then touching your eyes, nose or mouth.

Who is at risk: It is rare for avian or swine flu to spread to people. Anyone can get the flu, but children younger than 5 years of age, pregnant women, people 65 and older, and people with weakened immune systems are at high risk for serious flu complications. People who work closely with large numbers of birds and pigs, such as poultry or pig producers, are at risk of infection if their animals are infected. Anyone at high risk of serious flu complications planning to attend an agricultural fair or farm where swine are present should avoid close contact with the pigs and swine barns, including pig exhibitors and family members with high risk factors.

Signs in farm animals: Both birds and pigs can be infected with flu viruses without showing symptoms. If they do get sick, poultry flocks can experience a range of illness, from decreased egg production to extremely high death rates. Symptoms in pigs can include fever, depression, coughing, discharge from the nose or eyes, sneezing, breathing difficulties, eye redness or inflammation, and lack of appetite.

Symptoms in people: With both avian and swine flu, infected people can have symptoms similar to the human seasonal flu, such as fever, fatigue, lack of appetite, and coughing. They may also have red eyes, nausea, abdominal pain, diarrhea, and vomiting. In some people, serious complications from flu can occur, such as inflammation of the heart (myocarditis), brain (encephalitis) or muscle (myositis, rhabdomyolysis) tissues, and multi-organ failure (for example, respiratory and kidney failure).

Livestock Disease: Cause and Control

Published Mar. 2017|Id: AFS-3999

By
Joe G. Berry

The livestock industry is extremely important to the economy of Oklahoma and includes
not only commercial producers of meat or milk, but also purebred breeders and small
producers with a few animals. The success of any type of livestock operation is closely
related to the disease level of the animals.

Losses due to disease originate in many ways. Some are obvious, such as death, medication
costs, and condemnations at the processing plant. Others are sometimes less obvious,
such as poor growth, poor production, poor feed conversion, and downgrading.

The purpose of this fact sheet is to acquaint livestock producers with some basic
facts about diseases, their spread, and ways to minimize losses.

Disease Principles—Nature and Cause of Disease

Disease is an alteration of the body or body organs which interrupts or disturbs the
body’s function. Such disturbances often are recognized by detectable alterations
of body functions.

Etiology is the study of disease causes. A disease often results from a combination
of two or more causes: (1) the indirect or predisposing factors which may lower the
animal’s resistance and (2) the direct or determining factors which produce the actual
disease.

Predisposing causes of disease are referred to frequently as “stress” factors. Stress
factors include chilling, poor ventilation, overcrowding, inadequate feeding and watering
space, overmedication, etc. Times to be particularly aware of stress are at shipping
and weaning.

Direct causes of disease are: (1) bacteria, (2) viruses, (3) parasites, (4) fungi,
(5) nutritional deficiencies, (6) chemical poisons, and (7) unknown causes.

Infectious diseases are the greatest threat to livestock health. They are caused by
bacteria, viruses, rickettsia and fungi. Some protozoan diseases behave much as infectious
diseases and often are considered as such. Correctly, they should be classed with
the other parasitic illnesses.

When living agents such as bacteria enter the body and multiply, they cause a disturbance
of function, and disease occurs. Disease is caused by the chemical toxins (poisons)
produced by invading organisms. At one time some scientists believed that microorganisms
caused disease by mechanical objection of vessels or tissue spaces. This theory has
been disproved, and it is now clear that damage is caused by chemical substances.
But in some protozoan diseases, such as coccidiosis, mechanical damage to tissues
is an important factor.

All contagious diseases are infectious, but not all infectious diseases are contagious.
A contagious disease is one that is transmitted readily from one individual or group
of animals to another. An infectious disease is one produced by living organisms.
Most infectious diseases of animals are contagious; however, a few, such as aspergillosis,
are not.

The ability of an organism to cause disease in a host is known as its virulence or
pathogenicity. Many microorganisms unable to cause disease under most conditions may
cause disease under certain conditions. Therefore, such microorganisms would be considered
pathogenic in that particular host under the existing conditions. On the other hand,
some organisms almost always are pathogenic and produce disease when they enter the
body of a susceptible host. Some will invade the body of only one species of birds
or animals and are said to be specific for that particular species. For example, infectious
bronchitis virus will cause disease only in the chicken. Other organisms affect a
large number of species. For example, some of the Salmonella organisms affect a large
variety of species, including reptiles, rodents, domestic animals, poultry, and man.

The ability of an organism to cause disease is not a fixed characteristic. It depends
upon many factors, such as the ability to invade tissues and produce chemical toxin.
Often pathogenicity can be altered intentionally. This characteristic has been used
in developing some vaccines. Variation in pathogenicity or organisms also partially
explains why the same disease may present different forms and degrees of severity.

How Infectious Diseases Are Spread

Some common ways infectious diseases are introduced into and spread within herds or
flocks are:

1. Introduction of diseased animals.
2. Introduction of healthy animals that have recovered from disease but are still carriers
   or can still transmit the disease to other animals.
3. Contact with inanimate objects that are contaminated with disease organisms (trucks
   and trailers, feeders, waterers, etc. ).
4. Contact with carcasses of dead animals that have not been disposed of properly.
5. Impure water, such as surface drainage water.
6. Rodents and free-flying birds.
7. Mosquitoes. Insects-fowl pox and encephalitis are commonly transmitted by mosquitoes.
8. Shoes and clothing of persons who move from farm to farm.
9. Contaminated feed and feed bags.
10. Contaminated premises through soil, old litter, or bedding.
11. Airborne organisms, which do not spread far through the air, but this source of infection
    can be a strong factor in heavily populated livestock areas.

Body Defenses Against Disease

The body has a well-developed defense mechanism that must be understood and utilized
in controlling infectious diseases. Immunity is the ability to resist infection; however,
this ability can be overcome under certain conditions. Resistance is used interchangeably
with immunity.

An animal has two types of protective mechanisms: (1) those that hinder or prevent
invasion of organisms, and (2) those that combat agents which invade the body.
Mechanisms that hinder or prevent invasion of organisms include the intact skin and
mucous membranes, which create a direct barrier; secretions, such as mucous, which
tend to dilute and wash out invading organisms; and cilia (hair-like projections on
some mucous membranes), which, with wavelike action, move foreign material out of
such structures as the trachea (windpipe).

Mechanisms that combat agents invading the body include white blood cells and circulating
antibodies.

Immunity (resistance) is outlined as follows:

1. Innate or inherited
   1. Species
   2. Racial (Strain or breed)
   3. Individual
2. Acquired
   1. Active
      1. Resulting from having the disease
      2. Stimulated by vaccination with dead or living disease agents
   2. Passive
      1. Injection of antiserum
      2. Transferred from dam to offspring

While inherited immunity is important, acquired immunity is a more controllable reaction
that can be used intentionally by the producer. Acquired immunity is the reaction
the producer hopes to stimulate by the application of vaccines. The purpose of vaccines
is to stimulate an active production of antibodies by safe means. Active immunity
depends upon the production of antibodies within the body of each individual. Antibodies
are proteins associated with globulin fraction of the blood serum. Antibody production
is not understood completely, but antibodies apparently are produced by various organs
such as the liver, spleen, and bone marrow. In general, antibodies are specific for
the organism that stimulated their production: thus, immunity to one disease ordinarily
does not imply immunity to others.

Passive immunity is the transfer of antibodies from the individual in which they are
produced to another individual. This may be done by the injection of serum collected
from an immunized individual. Antibodies also are transferred from the dam to the
offspring at birth. Such passive immunity is an important consideration in vaccination
programs.

Manifestations of Disease

Detectable signs of disease are known as symptoms. Visible changes in the size, color,
shape or structure of an organ are known as lesions. Loss of body weight, decreased
production, reduced feed consumption, droopiness and lameness are some symptoms. An
enlarged liver, tumor on the intestine, or abscess in a lung are examples of lesions.
Many symptoms are general; they usually are seen in any diseased individual. Examples
are droopiness, loss of appetite, labored breathing, nasal discharge and diarrhea.

Herd or Flock Health Management

Sanitation is a much-used but poorly defined word. The usual implication is that sanitation
is a universally understood practice that may be applied to prevent all diseases.
This concept often leads to misunderstanding and disappointment. Good sanitation in
relation to one disease actually may provide conditions for the development of other
diseases. Although many good sanitation measures always should be applied, others
must be based on the nature of specific diseases. The ambiguity surrounding the term
“sanitation” can be avoided by using the expression “management and sanitation for
disease prevention.” This phrase then would be defined as all practices, specific
and nonspecific, that the livestock producer applies to prevent disease or reduce
severity and economic loss from diseases.

A standard disease prevention program that can apply on all farms does not exist.
But there are some basic principles that always should be observed. The following
practices aid in disease prevention:

1. Consult a veterinarian before planning a health program.
2. Select a well-known, reliable source from which to purchase animals, one that can
   supply healthy stock, inherently vigorous and developed for a specific purpose.
3. Keep animals separate according to source and age groups. To mix animals is an invitation
   to trouble.
4. Follow an “all-in, all-out” program.
5. Select a reliable commercial feed, or if farm mixing is done, mix carefully according
   to a dependable formula.
6. Provide an adequate supply of wholesome water. Avoid watering from surface tanks,
   streams, or ponds.
7. Carry out a precise vaccination schedule for each herd or flock. Work out the vaccination
   program with disease authorities in each state or local area.
8. Discourage persons other than the caretaker or essential personnel from visiting the
   barns and lots. This would include vehicular traffic.
9. Observe animals frequently for signs of disease, and if a disease problem develops,
   obtain an early, reliable diagnosis and apply the best treatment, control, and eradication
   measures for that specific disease.
10. Dispose of all dead animals by burning, deep burying, or disposal pit. This phase
    of management often is overlooked.
11. Maintain good records relative to flock or herd health. These should include vaccination
    history, disease problems and medication.

Many facts of disease prevention are acquired only through experience and a well-rounded
grasp of modern animal husbandry.

Joe G. Berry
Former Extension Poultry Specialist

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STOCK DISEASES ACT 1923
Definitions

STOCK DISEASES ACT 1923 – SECT 3
Definitions

New South Wales Repealed Acts

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This legislation has been repealed.

STOCK DISEASES ACT 1923 – SECT 3

Definitions

3 Definitions

(1) In this Act, unless the context otherwise requires:

“Abattoir” means any premises used for the slaughter of stock or for the
processing of carcasses of stock.

“Artificial breeding equipment” means any plant, machinery, equipment,
apparatus, utensil, additive, extender, diluent, material or other thing used
or intended to be used for or in connection with an
artificial breeding procedure.

“Artificial breeding material” means semen, ova and embryos of any stock.

“Artificial breeding procedure” means each of the procedures of collecting,
processing, handling, storing and distributing artificial breeding material
and includes artificial insemination and ovum transfer.

“Carcass” includes any portion of a carcass, and the hide, skin, hair, wool or
viscera of any stock.

“Department” means the Department of Industry and Investment.

“Director-General” means the Director-General of the Department.

“Disease” means:

(a) in stock–any disease, parasite or pest declared under
section 4 (a) to be a disease in stock for the purposes of provisions of this
Act, and

(b) in artificial breeding material–any disease, condition or
characteristic declared under section 4 (b) to be a disease in
artificial breeding material for the purposes of provisions of this Act.

“Fittings” includes stall fittings, utensils, implements, rugs, harness,
chains, headstalls, and anything commonly used for, with or about stock or
places where they are kept, and also includes artificial breeding equipment.

“Fodder” includes hay, straw, litter, grass, grass seed, green crop, grain,
corn, any type of stock food and the manure of any stock.

“Holding” means any land in respect of which a return of land and stock is
required to be furnished under section 58 of the Local Land Services Act 2013
.

“Infected” means:

(a) in relation to stock that the stock are diseased or have
been on land on which there are, or have been within the preceding 12 months,
diseased stock, or

(a1) in relation to artificial breeding material–that
stock bred from the artificial breeding material may, as a result of being
bred from the material, be affected by disease in the material, or

(b) in
relation to a carcass–that the carcass is of infected stock, or

(c) in
relation to land–that the land is land on which diseased stock are present,
or have been present during the preceding 12 months, except if that presence
consisted only in the transport of the stock by vehicle over the land.

“Inspector” means inspector appointed under this Act.

“Occupier” when used in relation to land includes a person in charge of land
as manager, agent or superintendent or otherwise.

“Ova” means:

(a) germ cells of female stock, or

(b) cells of female stock
that can develop into new members of the same species after maturation and
fertilisation,

and includes fertilised cells, embryos and foetuses up to the
end of the first third of pregnancy, but does not include cells or embryos of
an avian species of stock.

“Ovum transfer” means any operation by which an ovum is collected from female
stock and implanted in other female stock either by way of an intermediate
host animal or otherwise.

“Protected area” means land declared by the Minister to be a protected area
under section 11A.

“Protected (control) area” means land declared by the Minister to be a
protected (control) area under section 11A.

“Quarantine area” means any land which:

(a) is declared by the Minister to be
a quarantine area under section 10,

(b) is deemed to be a quarantine area
under section 11, or

(c) is declared by an inspector to be a quarantine area
under section 8.

“Record” means any book, account, document, paper or other source of written
information compiled, recorded or stored in written form, or on microfilm, or
by electronic process, or in any other manner or by any other means.

“Regulations” means regulations for the time being in force made under the
provisions of this Act.

“Semen” means the fluid produced by the reproductory organs of male stock and
includes:

(a) any part of that fluid, and

(b) spermatozoa in any fluid or
frozen medium.

“Stock” means horses, cattle, asses, mules, camels, sheep, pigs, or goats, or
any animals or birds or any eggs of any birds to which the Governor by
proclamation to be published in the Gazette may apply the provisions of this
Act.

Editorial note : For proclamations under this definition see Gazettes No
4 of 8.1.1999, p 33 and No 119 of 19.7.2002, p 5476.

“Travelling stock” means stock on any vessel, aeroplane or airship, or taken,
driven, or carried along any road or on any land other than that on which they
are ordinarily kept or pastured.

“Veterinary practitioner” has the same meaning as in the
Veterinary Practice Act 2003 .

(2) For the purposes of this Act (other than
section 1), a power to issue a proclamation, to make an order or to give a
notification, a notice or an authority shall include and shall be deemed
always to have included a power:

(a) to revoke or amend that instrument, or

(b) to extend or suspend for any period the effect of that instrument.

(3)
Where by any provision of this Act a person is required to do, or to refrain
from doing, some act or thing in relation to the sending of stock to an
abattoir for slaughter or to a saleyard for sale, that provision shall be
construed as requiring the person to do, or to refrain from doing, the act or
thing, whether or not the abattoir or saleyard, as the case may be, is
situated in New South Wales.

(4) A reference in this Act to fittings
contaminated with disease includes, in the case of a disease in
artificial breeding material, a reference to fittings contaminated with
infected artificial breeding material.

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U.S. approval of Biogen Alzheimer’s drug sends shares soaring, hailed as ‘big day” for patients

June 7 (Reuters) – U.S. regulators on Monday approved Biogen Inc’s (BIIB.O) aducanumab as the first treatment to attack a likely cause of Alzheimer’s disease despite controversy over whether the clinical evidence proves the drug works, sending its shares soaring.

Biogen said it has priced the drug, to be sold as Aduhelm, at $56,000 per year. After jumping more than 50%, Biogen shares closed up 38.3% at $395.85 – their highest closing level in over six years. U.S. traded shares of Japanese partner Eisai Co (4523.T) rose 56% to $116.03.

Aducanumab works by removing sticky deposits of a protein called amyloid beta from the brains of patients in earlier stages of Alzheimer’s in order to stave off its ravages, which include memory loss and the inability to care for oneself.

“This is good news for patients with Alzheimer’s disease. We’ve not had a disease modifying therapy approved ever,” said Dr. Ronald Petersen, an Alzheimer’s disease expert at the Mayo Clinic. However, he cautioned, “This is not a cure. It’s hoped that this will slow the progression of the disease.”

“I think this is a big day,” Peterson said. “But we can’t overpromise.”

Alzheimer’s is the sixth leading cause of death in the United States.

The Food and Drug Administration on its website said results from clinical trials of Aduhelm were mixed, but the drug was shown to reduce amyloid beta plaques in the brain, which is likely to benefit patients.

“We decided that the accelerated approval pathway fits well here,” Dr. Patrizia Cavazzoni, director of the FDA’s Center for Drug Evaluation and Research, told Reuters, explaining that this “allows for there being some residual uncertainty on the drug’s clinical benefits while making the drug available to patients rather than having to wait.”

Under that pathway, Biogen is required to conduct a confirmatory clinical trial, but has several years to do so, she said.

Biogen’s drug had been hailed by patient advocates and some neurologists eager to have an effective option for patients with the lethal disease. Other doctors said clinical trial results were inconsistent and more proof was needed.

“I’m surprised and disappointed,” said Dr. Caleb Alexander, a drug researcher at Johns Hopkins who was on the FDA’s advisory committee that voted against approval of Biogen’s drug.

“It’s hard to find a scientist, even one with ties to the manufacturer, who believes the totality of the evidence is persuasive.”

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U.S. health insurers and the U.S. government’s Medicare program will bear most of the cost of the drug, whose price will vary based on dosage and discounts.

Aducanumab was studied in patients with early disease who test positive for a component of amyloid brain plaques, but the FDA approval does not indicate that it is for a specific group of patients. That raises the possibility that the treatable population could be larger than anticipated.

A sign marks a Biogen facility in Cambridge, Massachusetts, U.S. January 26, 2017. REUTERS/Brian Snyder

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Still, most insurers are likely to limit their coverage of the drug to the type of patients who participated in the trial, said Steve Miller, chief clinical officer at health insurer Cigna Corp (CI. N).

He estimated that 85% to 90% of U.S. patients eligible for the drug are covered by Medicare.

“I would be very surprised if any payer, including Medicare, made it available for a broader population than the study population,” Miller said.

The FDA said if the post-approval trial fails to show Aduhelm works as intended it could be pulled from the market.

“I think they made the right decision. It makes the drug available to patients while requiring the company to do more research to prove its benefit,” said Dr. Howard Fillit, chief science officer of the Alzheimer’s Drug Discovery Foundation.

Some trial patients experienced potentially dangerous brain swelling. The FDA advised that patients who experience the side effect should be monitored but not necessarily taken off the drug.

Biogen has estimated that around 1.5 million Americans would be eligible for treatment with aducanumab, which is given by monthly infusion, raising concerns about costs to the healthcare system.

The number of Americans living with Alzheimer’s is expected to rise to around 13 million by 2050 from more than 6 million currently, according to the Alzheimer’s Association.

Wall Street analysts are now estimating peak annual sales for the drug ranging from $10 billion to $50 billion.

Development of aducanumab has been closely watched in Japan, which has a sizable elderly population and an estimated 4.6 million people living with dementia.

Eisai’s shares were untraded in Tokyo on Tuesday, bid up by their daily limit of 19%. The company has rights to 55% of profits on the drug in the U.S. and 80% in Japan and much of Asia. Eisai has another Alzheimer’s drug candidate called BAN2401 that is in Phase III trials.

Even so, Japan’s system for setting drug prices for its national health care system would likely limit annual sales to 100 billion yen ($914 million) “at the best,” according to Credit Suisse pharma analyst Fumiyoshi Sakai.

The medicine is seen as critical to Biogen’s revenue growth prospects as competition has hurt sales of its mainstay multiple sclerosis drug, Tecfidera, as well as its other growth driver, Spinraza for spinal muscular atrophy.

Wall Street analysts had forecast that an FDA approval of Biogen’s drug could reinvigorate a field that has been abandoned by many large pharmaceutical companies after a long string of failures. Current treatments only try to alleviate symptoms.

Shares of companies developing Alzheimer’s treatments also jumped, with AC Immune SA (ACIU.O) closing up 27.7% and Anavex Life Sciences (AVXL.O) up nearly 8%, while shares of Eli Lilly and Co (LLY.N) closed 10% higher and Axsome Therapeutics (AXSM.O) finished up 6.4%.

($1 = 109.4000 yen)

Reporting by Deena Beasley in Los Angeles; Editing by Bill Berkrot

Our Standards: The Thomson Reuters Trust Principles.

90,000 Seven falls in stock markets – Money – Kommersant

1907. The Panic of the Rich and the Panic of 1907. From January 1906 to November 1907, the Dow Jones Industrial Average (DJIA) fell from 103 to 53 points. The index broke through the 90 mark after the San Francisco earthquake in April 1906. And in October, the Bank of England decided to raise the interest rate from 4 to 6% in order to stop the flow of gold to the United States: British insurers paid for San Francisco. Stopped. On March 14, 1907, US borrowing rates reached 15%.DJIA fell 8.3%. It was the “rich man’s panic.”

The 1907 panic began in October, when an unsuccessful aggressive buyout of United Copper Company shares led to a chain of bankruptcies and massive outflows of funds from banks. The DJIA repeated its 1906 record only in November 1916.

As a result, in 1913 the American central bank – the Federal Reserve System – was created.

1929-1933. The Great Depression. The recession lasted from August 1929 to March 1933.This time was three of the ten worst DJIA years: -34% in 1930, -53% in 1931, and finally -23% in 1932. The decline in the index began in 1929 and was smooth at first. But on “Black Monday” October 28, the index lost 13%, on “Black Tuesday” 29th – another 12%. Then the adjectives lost their meaning: in the top 20 worst days in DJIA history, eight days belong to the years of the Great Depression.

The DJIA returned to its previous levels only in November 1954, having experienced four more economic recessions and two protracted falls from local highs along the way.

One of the results of the crisis was that Keynes’s point of view on stimulating demand by increasing the money supply won out in economics.

1973-1975. Oil crisis. The DJIA hit another high of 1,052 in January 1973, and it took 694 days to find another bottom at 578 (the index only “rallied” in 1983). However, the British FT 30 Index was even less fortunate: it lost more than 70%. The years leading up to the decline marked the end of the Bretton Woods system: the growing US balance of payments deficit pushed President Richard Nixon to abandon the dollar peg to gold (August 15, 1971) and currency devaluation (December 17, 1971 and February 13, 1973).Attempts to curb inflation by means of state control over the growth of wages and prices were unsuccessful: after the end of the freeze, the effect of deferred inflation was triggered. When OPEC imposed an embargo on oil supplies to countries that supported Israel in October 1973, the fall in markets accelerated. In November 1973, the United States entered a recession that lasted until March 1975. The price of oil by the end of 1974 rose from $ 3 to $ 12 per barrel. And the term “stagflation” has entered the lexicon of economists.

1990-2003.Japan. Lost decade. On December 29, 1989, the Tokyo Stock Exchange closed higher. The Nikkei index reached 38,916 points. It was the last good day of his life. On January 4, 1990, a fall began, and the index did not return to its previous heights, finding the first bottom in August 1992 at 14,338, and then sliding down twice more.

The collapse of the bubble was predetermined by the policy of the Bank of Japan, which in May 1989 set a course to increase the discount rate. In August 1990, it reached 6%, only to fall below 1% in 1995 and remain near zero for all subsequent years.Until 2000, Japan, following Roosevelt’s recipes, increased the volume of public works and investments in capital construction, but to no avail. The growth of the money supply at zero rates did not help – Japan fell into the liquidity trap predicted back in the 1930s.

1997-1998. Collapse of LTCM and default of the Russian Federation. The RTS reached its pre-crisis maximum (571) on October 7, 1997. The fall began after the collapse of the LTCM fund, the Asian crisis and the decline in oil prices. A year later, on October 6, 1998, the market met the historical low of the RTS (39) – the result of the decisions on August 17 and the ill-considered budgetary and debt policy of the previous years.

The devaluation allowed the Russian industry to implement the import substitution strategy. And the RTS returned to the 570 mark only in the fall of 2003.

2000. The collapse of the dotcoms. The end of the century saw the explosion of Internet companies. Most of their business models were ineffective, spending mostly on advertising, but belief in the “new economy” fueled their stock prices. In 1999, the NASDAQ hi-tech stock index nearly doubled.On March 10, 2000, it reached an all-time high – 5132 points – and began to move down. The fall was not a landslide, but by May 26 the index reached 3151. In the fall of 2000, there was a new landslide. As a result, about half of Internet companies disappeared. The NASDAQ returned to the previous meteoric rise and fall levels in 2004.

2008 World financial crisis. The fall in stock markets, which began in October 2007, was the result of the mortgage crisis, which gradually turned into a financial one.On September 15, 2008, one of the five largest investment banks, Lehman Brothers, filed for bankruptcy. The rest during the crisis were either oversold, or for the sake of getting help from the Fed ceased to be investment ones. During the crisis, major stock indices fell more than twice (for example, the DJIA – from 14,165 on October 11, 2007 to 6507 in March 2009). The Russian RTS fell five times – from 2491 at the end of May 2008 to 498 by the end of January 2009. It fell according to its schedule, accelerating after Vladimir Putin’s promise to “send a doctor” to Mechel and the war in South Ossetia.The remedy for the crisis was the same in all cases: the placement of public funds in the affected banks, injections into the stock market, support for the auto industry, etc.

90,000 Why the stock market spikes

Many investors in the stock markets today are experiencing motion sickness symptoms. And it’s not just a drop in quotations, because of which the US stock indexes went into negative territory last Thursday, having lost all growth since the beginning of the year. Sharp market jumps, which have become a constant phenomenon in recent years (over the past month and a half, the indices have gone negative for the third time), are much more on the nerves.

Over the past couple of weeks, the Dow Jones Industrial Average, for example, has repeatedly rallied or fell 500, 600, or 700 points throughout the day. Last Tuesday, it fell by a staggering 799 points, or 3.1%, in a few hours.

Although such races have happened before, they usually happened after some dramatic event. Now, it seems, the catalyst is becoming a relatively modest phenomenon, say, the words of US Federal Reserve Chairman Jerome Powell that interest rates are close to neutral or a tweet by Donald Trump, who called himself a “man of duties.”Why? One of the assumptions is that shy investors cannot decide on the prospects for the market and the economy. The Dow Jones’ 799-point crash, for example, was attributed to the fact that the tweet about the “man of duties” signaled increased tensions in the trade war and, consequently, a slowdown in growth.

However, such an explanation does not seem to be sufficient given the magnitude of the volatility. A clearer picture can be drawn by borrowing an idea from Seth Klarman, founder of hedge fund Baupost, and looking at the controversial concept of leverage.

Investors who rush this word to the right and to the left today often mean only “debt”. This is not surprising: in 2008, we realized again that when financial institutions or investors use borrowed funds to gamble in the market, they suffer disproportionately large losses if prices fall. This forces them to sell assets, which provokes sharp market surges, especially when debt is “wired” into (or hidden in) derivatives.

But debt alone does not explain today’s market swings from side to side.The risks associated with the insolvency and leverage of banks and insurance companies are now low, the Financial Research Administration, an independent bureau run by the US Treasury Department, said in November. And although the leverage of hedge funds is increasing (now they have $ 2.7 trillion in debt for $ 3.1 trillion in assets versus $ 1.9 trillion for $ 2.7 trillion, respectively in 2015), by historical standards it does not (yet) reach extreme values.

Therefore, it is necessary to consider the concept of leverage in a broader sense – not only in connection with debt.It itself appeared in the 18th century. from the word lever, meaning “lever,” a tool used in agriculture or industry to gain power. In the 1930s. financiers borrowed this concept to describe a similar principle – obtaining a profit in profitability through the use of borrowed funds.

But the principle that allows you to get additional profit in the market can be implemented in different forms. Klarman, for example, is interested in the concept of “psychological leverage” – a situation in which everyone is so confident that the market will move in a certain direction that even a small reversal can provoke panic, which will fuel itself, increasing the movement in the opposite direction. …“In 2008, we had huge hidden [debt] leverage,” he says. – Now the financial leverage is less, but there is a psychological one.

Terry Duffy, CEO of exchange operator CME Group, agrees. “Seventy-one million people in America are millennials who have never seen a market crash in their [investment] lives. And if the market is shaking, they don’t know what to do, especially given that information is spreading so quickly today, ”says Duffy. Ray Dalio, founder of Bridgewater Associates, the world’s largest hedge fund, recently commented, “The world as a whole is focused on gaining from growth.If there is a recession, I do not think that something can seriously protect investors. ”

In addition, there is a phenomenon that Klarman calls “algorithmic leverage.” So, twenty years ago, investors learned from their own experience that complex, poorly understood derivatives can seriously increase losses: remember the hedge fund Long-Term Capital Management, which used the work of the Nobel laureates and got burned by the default of Russia? Today, algorithmic trading, exchange-traded funds and passive investment strategies can sometimes have an equally unpredictable and mysterious effect on the markets.

Of course, these are useful investment innovations that can increase liquidity and market depth during calm times. But in times of uncertainty, “the question arises whether they can provoke destabilizing price dynamics, increasing the impact of trading models used by investors,” experts from the Bank for International Settlements note. The irony is that, as it is believed, rational computer programs are created by programmers and financiers who are not alien to anything human and who think the same way.This creates a “computer crowd effect” where trading programs respond in the same way to the same small signals, exerting an excessive cumulative effect on the market. This, if you like, can be called a “robotic arm”.

Is there any solution to this problem? It is far from obvious in a world where investors have been overly optimistic about the market outlook for several years. But there is one little consolation: history has shown that bullish trends usually end in unexpected declines, rather than in a series of disorienting spikes.In this sense, the current situation in the markets is really unusual. But it does give investors dizzy with seasickness the opportunity to leave the ship.

Translated by Mikhail Overchenko

The opinions of experts of banks, financial and investment companies presented in this section may not coincide with the opinion of the editorial staff and do not constitute an offer or recommendation to buy or sell any assets

Viral stock disease exchanges.Competent look. DailyComm.ru

PandaLabs, Panda Security’s malware analysis and detection lab, warns that there is a direct correlation between recent stock market volatility and growth the number of new threats. According to experts from the PandaLabs laboratory, the two events are much more closely related than previously thought, and the recent volatility in the stock market has accelerated the growth of targeted cyber attacks and their corresponding impact on the economy over the past month and a half.In addition, analysts at PandaLabs believe that the recent surge in malware growth may be due to the fact that cybercriminals now have fewer likely targets, a result of consolidation within the banking industry.

“When we looked at the specific effects of cybercriminals on the economy during a downturn, we found a striking relationship: the criminal economy is very closely tied to the economy itself,” says Luis Corrons, CTO of PandaLabs. “Based on extensive research and analysis of the emergence of new malware samples, we believe that criminal organizations closely monitor the performance of the market and adapt to it, if necessary, for maximum profit.”

A new strategy is currently under development, according to PandaLabs, to respond to the criminals’ response to the consolidation in the banking sector, manifested in the introduction of multimillion-dollar packages by several governments to fight the crisis. As a result of this consolidation, fewer banking institutions will continue to exist in the long term and a sense of instability in the financial community will turn the latter into a less attractive target, which has increased the number of other types of malware, such as adware, which would normally be second only to Trojans.

“Cybercriminals are forced to increase their activity in order to reach more users with their campaigns designed to get money, especially during periods of economic instability. in which unsuspecting customers were forced to carry out an online transaction – for this, criminals used such a technique as phishing (banking sites) “- explains Corrons.” According to our information, fraudulent, fake antivirus campaigns collect more than 10 million euros monthly for the shadow economy. “

The following data reflects the key findings of PandaLabs:
• On average, the US stock market declined from 3 to 7 percent from September 1 to October 9. However, the activity in the “malware markets” was the opposite: it increased significantly, while activity in the stock markets fell.
• From September 5 to September 16, all Dow Jones Industrial Average, NASDAQ, S&P 500 and Composite Index went from positive 0.0 to near negative 3.0 percent or below (see chart).Picture 1). During the same period, the Spanish IBEX 35 index and London FTSE 100 also experienced significant losses. At the same time, there was a significant increase in malicious threats that occur per day; for example, from September 8 to 10, the number of threats detected per day increased from 10,150 to over 24,000. 90,087 • From September 14 to September 16, stock market values ​​fell from -0.5 to -5.5 percent, while the number of daily threats increased by 50 percent every day, from 8276 on September 14 to 31404 on September 16 (see.Figure 2).

The charts below show data clearly showing that when the stock markets get worse, cybercriminals capitalize on weakened economic performance to generate huge financial returns. For example, Figure 3 shows the overall decline in the DJIA, NASDAQ, S&P 500, FTSE 100, IBEX 35 and Composite Index over a month and a half, and shows several points when significant losses occurred. Comparing this graph with the graph of the evolution of malware (Figure 4) from September 1 to October 9, it is clear that when the market is unstable, cybercrime increases significantly.

Figure 1 – Changes in the stock market (from September 5 to September 15) – source: moneycentral.msn.com

Figure 2 – Evolution of threats with key points (from 5 September to September 15) – source: PandaLabs

Figure 3 – Stock market changes (from September 1 to October 9) – source: moneycentral.msn.com

Figure 4 – Evolution of Threats with Key Points (September 1 to October 9) – Source: PandaLabs

“As it becomes clear when comparing this data, there will be no end to the persistence and growth of cybercrime, as well as its attempts to use malware to obtain financial profits “, says Corrons -” Despite the economic situation, we are aware that cybercriminals are constantly reworking their strategies and, obviously, gain live up to the downturn by hunting unsuspecting victims.By remaining vigilant and aware of all of this, we are better equipped to protect ourselves and the economy from the truly real dangers of malware. “

Source: DailyComm

90,000 The permanent head of the St. Petersburg stock exchange dies after a short illness

Viktor Nikolaev, the founder and permanent head of the St. Petersburg stock exchange, died on Tuesday, July 13, at the age of 81.

Famous avant-garde artist Boris Koshelokhov dies of coronavirus in St. Petersburg

Coronavirus

Famous avant-garde artist Boris Koshelokhov dies of coronavirus in St. Petersburg

As noted on the website of the exchange, the death occurred as a result of a short illness.Details of the death were not disclosed.

“Viktor Vasilyevich deservedly enjoyed the respect of the professional community, had a high business reputation among financial market participants. In addition, he took an active public position, participating in the governing bodies of various public organizations,” the obituary says.

Nikolaev was born in 1941 in the Pskov region, in 1978 he graduated from the Leningrad Institute of Finance and Economics. After that he worked in leading positions in the trusts “Lenzhilsnab”, “Glavleningradstroy”, “Lenzhilpromkomplekt”, held the post of the first deputy head of the Glavsnab of the Leningrad City Executive Committee.In 1991, under his leadership, the commodity and stock exchange “St. Petersburg” was created.

Earlier in St. Petersburg, the former director of the center for difficult teenagers “New Generation” Mikhail Dmitriev died of coronavirus. The teacher died at the age of 61.

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90,000 people, knee pain, stock illustration, pain, sick, healthy, problem, illness, explore, people

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90,000 Arab Gulf Stock Exchange Recovers After Two-Day Sharp Drop

Major stock markets in the Arabian Gulf closed higher on Sunday thanks to low-price share purchases following sharp losses in the previous two sessions and advances in clinical trials for a possible treatment for Covid-19 disease.

On Friday, Gilead Sciences raised the participation rate in experiments with the experimental drug Remdisever in patients with acute Covid-19 disease to 3,600 patients. But analysts and the company are calling for caution, drawing conclusions from preliminary data, Reuters reported.

In Dubai, the index rose three percent as the share of its largest bank, Emirates NBD, jumped 3.7 percent ahead of a board meeting to approve first quarter results and Emaar real estate shares rose by the same percentage.

On Friday, the Dubai government tweeted that the Emirate of Dubai has extended its 24/7 curfew to combat the outbreak of the new coronavirus.

The Abu Dhabi Index rose 2.9 percent thanks to a 4 percent rise in the country’s largest bank, First Abu Dhabi.
The main index in Saudi Arabia rose 0.7 percent, Al Rajhi Bank added 0.7 percent and Saudi Telecom shares gained 1.4 percent.

Saudi Arabia’s Finance Minister Mohammed al-Jadaan said the Kingdom is facing the current global crisis from a position of strength, given its financial position and huge reserves, as well as relatively low public debt.

The Qatar Index rose 0.5 percent, the Commercial Bank of Qatar increased 2.7 percent and the Islamic Bank of Qatar rose 0.7 percent.
Qatar, which has closed part of an industrial zone where many migrant workers work and live, reported 5,008 cases, mainly among foreign workers who were quarantined due to exposure to cases of Covid-19 disease caused by the coronavirus.

Source: https://sptnkne.ws/CeEq

Trump’s illness triggered a surge in volatility

In addition to the already existing election fever, the news that Trump fell ill with coronavirus was added.This provoked a short-term risk-off, the strengthening of the yen and the dollar. At the beginning of the week, however, the demand for risk recovered somewhat. On the whole, our opinion was justified that the level of 94.6 on the dollar index would stand. Having bounced down from this level, the dollar index also tested from below the previously broken long-term long-term growth trend, which began back in 2008.
This is an additional argument in favor of the fact that a new cycle of dollar weakening has begun, which may last for several years.In this context, we are still determined that any attempts to strengthen the dollar should be sold, and these attempts themselves are very likely on the eve of the US elections.

Thus, we suggest selling the dollar against a basket of currencies when the dollar index attempts to rise to 94.6 and even slightly higher. The positions already taken against it should be held for the time being, since the approach to 94.6 may not happen. In the short term, the situation is still uncertain. Consolidation by the end of the week EUR / USD is again above 1.175 will be an additional argument in favor of the fact that the upward correction of the dollar, which we expected, ended at 1.16, and the target is already ahead in the region of 1.21 … 1.23.

USD / JPY, which sharply sagged due to Trump’s illness, bounced back above 105.5. In the dynamics of the pair, as often happens, two tendencies collided: the recovery in the demand for risk pushes it up, but the general dynamics of the dollar index, associated with the Fed’s policy and the dynamics of rates, pushes it down. In general, a downtrend has formed in the pair since summer, and we propose to work in it in the coming weeks from the short, which should be gaining above 105.6, since surges in volatility are likely to occur. Fixation at the end of the day above 105.8 will cancel this recommendation, since will give a signal for the medium-term growth of the pair.

GBP / USD is still in line with EUR / USD with the adjustment that the pound is oversold on fears of Brexit without a contract. We turned out to be right that this oversold level was somewhat discharged, but there is no more certainty in the relations between the EU and Britain, so the medium-term prospects remain unclear. Looking at EUR / GBP, we can state that from a technical point of view, the return is below 0.91 is a good sign for bears and one can carefully trade short from this level.

Brent oil was pushed to new local lows on a spike in volatility at the end of last week, but it has been recovering since the beginning of the week. We believe that the retracement trend will continue, a return above 41.5 will confirm the bullish scenario. In this regard, commodity currencies will receive additional support.

Ruble rate
USD / RUB met our expectations of a correction from 79, however, the war in Karabakh interrupted the correction.The ruble remains at the mercy of large political risks. For those who are ready to take them upon themselves, we recommend selling the pair when trying to break back up 79.

Stock Market
Friday’s sale thwarted the S & P-500’s attempt to return above spring highs, and this is a bad signal for the bulls. We propose to trade the corridor 3300 – 3390 (depending on the chosen tactic, either on a rebound from the borders in a sideways scenario, or on a breakout in a trend scenario). The RTS remains under pressure, but may be supported by the rise in oil.In addition, the range of 1100 – 1150, where the index has dropped now – the range of May consolidation. We do not expect the RTS to easily come out of it and think that it is worth buying it for the short-term if it attempts to decline to the 1100 area.