After obtaining one of the infected leaves, we will use the dichotomous key, found in Leaf Miners of North America (Eiseman 2019), to separate the data on mines made by different leaf miners. We will then measure the path of the leaf mine from the insertion point to the first vein crossing. Through our observations, we noticed some leaf miners show avoidant behavior (Figure.1), and travel to the edge of the leaf seemingly to avoid the veins. Even though this behavior does go through ends of the veins, we are defining the first crossing as the first time the mine crosses in the inner 80% of the vein. We intend to measure the first crossing, categorize the crossed vein as asymmetrical or symmetrical, and measure to the next crossing of opposite type. We will measure the total length of the mine, and measure the distance from the insertion point to the closest symmetrical vein in order to determine if the symmetrical region is accessible. We also plan on measuring the width of the mine at each crossing in order to determine if crossing is a function of size or the structure of the leaf.
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Discussion (ask about abbreviating aym and sym to save space)
A few generalizations can be made about the data collected. Of the miners that crossed veins, 75% of them crossed in asymmetrical regions first, which is consistent with the idea that females choose to lay their eggs in asymmetrical regions (Moller, 1999). The smallest asymmetrical vein crossing was at a width of .2mm and was complete, however, the closest symmetrical vein was at a growth of .261mm and the mine was left incomplete. Every leaf mine was long enough to be able to access a symmetrical area, but only 33% of miners proceeded to do so. It appears as though leaf miners do not have a strong tendency for or against symmetrical areas. Cursory observations suggest that symmetrical regions may slow the growth of the leaf miner. Since the quality of the leaf can change between symmetrical and unsymmetrical regions (Moller, 1999), this may be an area for future study.
Future Work (bullet list)
Identify instars of S. multispicata
Relate instars to specific widths of mines
Compare growth in symmetrical regions to asymmetrical regions
This is the updated methods for what we did (or at least what I did)
Approach and Analysis
This experiment aims to sample leaves from every available Elm species on the University of Massachusetts Amherst campus. There are currently 293 Elms on campus, and 16 unique species (Beals). When considering unique species, the Smooth Leaf Elms, a and b, were considered one species, and spp. was ignored because the species cannot be identified. Two of each Elm species will be selected through the use of a random number generator. The selected Elms will be searched for evidence of leaf miners by observing any accessible branches of the tree. Once a leaf miner is found, a circular marking sticker will be placed on the leaf, the next 9 closest leaves will be documented for leaf miner presence, and the branch will be marked with flagging tape. A picture will be taken with the trees identification number, and with all ten leaves visible in the same plane of view. A second picture of the entire tree, with the flagged branch visible, and the trees designation number, will also be taken. After, the leaf with the median amount of leaf mines will be collected in a single use marked bag for further analysis. All observed leaf miner larva will be collected from the infected tree, and placed in the bag with the infected leaf.
To sample different Elms, on the University of Massachusetts campus, in order to find leaf mining insects, and study their ovipositional tendencies.
Specific Aim 1
To sample each species of Elm present on the UMass Amherst campus, and to survey them for leaf mining insects. Pilot data shows, leaf miners inhabiting a resistant American Elm (U. americana, Brewer, personal observation). This leaf miner appears similar to Stigmella multispicata when identified using the dichotomous key found in Leafminers of North America (Eiseman 2019), however, this leaf miner has only been observed on Siberian Elms (U. pumila). We will sample of each Elm species on campus in order to observe any new potential hosts of this leaf miner.
** Normally I try to draft something for the class, or with something specific in mind, but my day started 17 hours ago and tomorrow starts in 5 hours so Im just going to write something a bit random... after careful thought all Ive got is my day so thatll be that. I suppose Ill try to organize it into sections based on good and bad events
My day has contained events that are both postive and negative with their respective instances. I brought my car in to deal with an emergency recall, at 8 am, which was supposed to last an hour. Two other recalls were also issued, and never dealt with by previous owners, and so my hour visit had turned into three and half hours of waiting. I did not recieve my car back and had to leave the dealership for my 10 hour shift at work. I had made a stop at Walmart, in order to eat at subway, and encountered my coworker. She insulted me, and then left me extremely confused as I went to eat lunch. I then had 6 hours of overlapping shift with that coworker.
I had three emergency recalls on my vehicle, however, one of them was a computer malfunction that was easily addressed. The other was for the control arms in my vehicle. These are getting replaced for free, and after a chat with the manager of the store, they are trying to convince Subaru headquarters that the faulty control arms damaged my axles, which were really worn down from the rough conditions of the roads near my home, and try to get them replaced for free. Overall this is saving me considerable money, and even if the axles arent replaced, they still have to do a 100$ alignment that I was planning on getting, and saving me at least that much. I had also misheard my coworker, and she did not actually try to insult me, and so my shift passed with far less stress than anticipated. Also, a stipend was issued to Summer Bridge Program students that I happen to be included in.
The methods used to collect the data for our research proposal I felt wasnt defined very clearly, although they were designed in a rushed manner. As we collected the leaves, the first thing was the termination point when the miners werent found. We had walked to the siberian elms first and had found no leaf miners, however, after circling both trees I did attempt to climb a tree in order to investigate more thoroughly. Earlier I had injured my shoulder lifting and so I was unable to hold my body weight with that arm and couldnt climb the tree. Other collection methods that were unclear, the first found leaf mine was to be marked and the next 10 leaves around it counted. The wording was slightly unclear, and so we counted the next 9 leaves and counted the found leaf as number one. We collected the medium number of mines, but tried to collect leaf 1 if that had the median number of mines. Another missing thing in the methods we followed, was that leaves taken should be whole if possible, and without any other breaks or damage. This was specific to my group as Prof. Long has mentioned, in order to look at the oviposition sites we should omit data where we cant tell if a leaf miner had caused damage or if the leaf had suffered damage. Overall these are just my thoughts on the data collection performed today
Leaf Miners are insects that can cause damage to the plant populations they target. These insects feed on the mesophyll inside of leaves, and leave the leaf vulnerable to infection (Bernardo et. al 2015). Over time, this can destroy the populations of the host plants. A leaf miner, likely to be Stigmella multispicata, was found infesting a Resistant American Elm (Ulmus Americana). The only known host for multispicata is the Siberian Elm (U. pumila), which suggests that this insect has swapped hosts. We aim to collect data on all Ulmus present on the University of Massachusetts Amherst campus and determine the presence of leaf miners, and the characteristics of infected leaves. We intend to sample two trees of every species, as far apart as possible on campus, as well observe the mine locations relative to the symmetry of the leaves in order to understand the selection tendencies of the leaf miner. It is important to document and understand these invasive insects because of the possible threat they represent to Elms. The possibility for leaving the elms susceptible to infection, while already being under threat of the dutch elm disease, makes the careful observation of such insects necessary in order to insure the continued existence of the Elm.
Invasive pest species can cause the health of host plants decline and eventually result in the death of host plant (Bernardo 2015). It is important to track the ability of invasive species to infect new hosts in order to gain an understanding of any pest control measures that must be taken. Our study intends to provide data one such pest, and possible factors that may provide insight into the patterns by which these insects select their host. This information may prove useful to future researchers in determining the prevalence and threat these insects may pose to Ulmus genus in the future.
Approach and Analysis: This experiment aims to sample leaves from every available Elm on the University of Massachusetts Amherst campus. There are currently 293 Elms on campus, and 16 unique species. Two of each Elm species, to be chosen in a manner that allows them to be as far apart as possible, will be searched for evidence of leaf miners by observing an accessible branch of the tree. The branch will searched for leaf miners on the last foot of the branch. All observed leaf miners will be removed from the infected tree. The leaves will be kept in a bag marked with the specific identification of the tree.
OUTLINE FOR GENTAMICIN AND VANCOMYCIN INTERFERENCE…..
Interference in medical tests
some type of interference exists.
Yadav S, Sanjaya KC Interference of drugs on clinical
chemistry—shall we start thinking?
Analytical interference defined.
Nikolac N. Ispitivanje interferencija. In: Simundic AM, ed. Upravljanje
kvalitetom laboratorijskog rada. Zagreb, Croatia: Medicinska naklada;
2013:51– 64. 3.
Dodig S. Interferences in quantitative immunochemical methods. Biochem
Incorrect test results.
Lippi G, Becan-McBride K, Beh´ulova´ D, et al. Preanalytical quality
improvement: in quality we trust. Clin Chem Lab Med. 2013;51(1):229–241.
Kailajarci M, Takala T, Gr ¨ onroos P, et al. Reminders of drug effects on ¨
laboratory test results. Clin Chem. 2000;46(9):1395–1400.
Nikolac N, Simundic AM, Miksa M, et al. Heterogeneity of manufacturers’ declarations for lipemia interference—an urgent call for standardization.
Clin Chem Acta. 2013;426(1):33–40.
Also, the knowledge of laboratory staff and clinicians about possible drug interferences is often overlooked or unknown.
Sonntag O. Quality in the analytical phase. Biochem Med.
Classification of interferences
Interferences are classified as endogenous or exogenous.
An endogenous interference originates from the substance found naturally in the
patient’s sample, like bilirubin, hemoglobin, glucose, antibodies, or proteins,
with hemolysis, icterus, and lipemia as the most common interferences.
An exogenous interference results from substances not naturally found in a patient’s
specimen, like drugs, their metabolites and additives, herbal products, or other
Interference effect depends on the concentration of an interfering substance, but not necessarily in a proportional way.
Drugs can interfere with laboratory measurements via several mechanisms.
Biological interferences occur when a drug activates one of the mechanisms, like
induction of hepatic microsomal enzymes, enzyme inhibition, or drug
displacement from protein-binding site.
However, these changes reflect a true state in the human body and thus are not
considered analytical errors. Analytical (or chemical) interference is present when
a drug causes falsely decreased or increased results of laboratory parameters.
Mechanisms of interference often include structural similarity of the drug to the
tested analyte, drug inhibition of the reaction used in the analyte measurement,
or changes in the structural integrity of the matrix (ie, viscosity or turbidity).
However, these effects can often go unrecognized in the laboratory because of
the lack of relevant information about patient drug therapy or unavailability of
methods for drug concentration measurement.
Kroll HM, Elin JR. Interference with clinical laboratory analytes. Clin Chem. 1994;40(11):1996–2005. 16.
Forman TD, Young SD. Drug interference in laboratory testing. Ann Clin Lab Sci. 1976;6(3):263–271. 17.
Benet LZ, Sheiner LB. Pharmacokinetics: The Dynamics of Drug Absorption, Distribution and Elimination. New York, NY: Macmillan; 1985:13– 22. 18.
Caraway WT, Kammeyer CW. Chemical interference of drugs and other substances with clinical laboratory test procedures. Clin Chim Acta. 1972;41(1): 395–434. 19. Kroll MH, Ruddel KW, Blank DW. A model for assessing interference. Clin Chem. 1987;33(7):1121–1123.
Seems these references cover the whole paragraph
Introduce objects of interest
Introduce function of objects
. Review of vancomycin-induced renal toxicity: an update
Factors impacting unbound vancomycin concentrations in different patient
We hypothesized that high drug concentrations might affect results of clinical