utc faculty recruitment workflow

 

Faculty Recruitment Workflow 

https://utc.teamdynamix.com/TDClient/2717/Portal/Requests/ServiceDet?ID=52650

Purpose

This workflow, which is housed within Dynamic Forms, provides a department head, or their designee, a path to request for new or vacant faculty or staff position to be advertised in our university's Taleo system. This workflow is Questions about concerning this process or form can be directed to academic-affairs@utc.edu. 

workflow for Seurat and Velocity

 

#workflow for Seurat and Velocity

#open Rstudio on VNC desktop

rstudio

library(Seurat)

#load in 10x data

sample1=Read10X(data.dir = 'path1')

sample2=Read10X(data.dir = 'path2')

#convert 10xdata into Seurat object

sample1.seurat=CreateSeuratObject(sample1, project = 'ID1')

sample2.seurat=CreateSeuratObject(sample2, project = 'ID2')

#calculate percent mito content

sample1.seurat[['percent.mt']]=PercentageFeatureSet(male5mo.seurat, pattern = 'mt-')

sample2.seurat[['percent.mt']]=PercentageFeatureSet(female5mo.seurat, pattern = 'mt-')

#show basic QC parameters for samples

VlnPlot(sample1.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(sample2.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

#can filter based on top/bottoms of violins to remove outliers e.g.

sample1.seurat=subset(sample1.seurat, subset = nFeature_RNA > 200 & nCount_RNA <20000 & percent.mt <5)

sample2.seurat=subset(sample2.seurat, subset = nFeature_RNA > 500 & nCount_RNA <20000 & percent.mt <5)

#remake vlnplots to compare vs before

VlnPlot(male5mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(female5mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(male22mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(female22mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

#normalise data

sample1.seurat <- NormalizeData(sample1.seurat, verbose = FALSE)

sample2.seurat <- NormalizeData(sample2.seurat, verbose = FALSE)

sample1.seurat <- FindVariableFeatures(sample1.seurat)

sample2mo.seurat <- FindVariableFeatures(sample2.seurat)

integ.anchors <- FindIntegrationAnchors(object.list = list(sample1.seurat, sample2.seurat))

combo.seurat <- IntegrateData(anchorset = integ.anchors)

# Run the standard workflow for visualization and clustering

combo.seurat <- ScaleData(combo.seurat, verbose = FALSE, vars.to.regress=c('nCount_RNA', 'nFeature_RNA', 'percent.mt'))

combo.seurat <- RunPCA(combo.seurat, verbose = FALSE)

# UMAP and Clustering

combo.seurat <- RunUMAP(combo.seurat, dims = 1:10)

combo.seurat <- FindNeighbors(combo.seurat)

combo.seurat <- FindClusters(combo.seurat)

combo.seurat$cluster_by_genotype = paste(Idents(combo.seurat), sep='_', combo.seurat$orig.ident)

DefaultAssay(combo.seurat)='RNA'

combo.seurat=CellCycleScoring(combo.seurat, s.features = stringr::str_to_title(cc.genes.updated.2019$s.genes), g2m.features = stringr::str_to_title(cc.genes.updated.2019$g2m.genes))

#command to find top enriched genes in each cluster vs all other cells is FindAllMarkers()

FindAllMarkers(combo.seurat, assay='RNA', only.pos=T)

#output is pasted in terminal, either save it to an object e.g. x=FindAllMarkers() or write it out into a text file

write.csv(FindAllMarkers(combo.seurat, assay='RNA', only.pos=T), file='combo_allmarkers.csv')

#command to compare two specific clusters is FindMarkers()

write.csv(FindMarkers(combo.seurat, group.by = 'cluster_by_genotype', ident.1 = '16_UOmet', ident.2 = '16_UOcont'), file='combo_cluster16_UOmet_v_UOcont_DEG.csv')

#group.by can be column name in combo.seurat$ 

#ident.1 and ident.2 must be set, if only ident.1 set then it compares against all other cells 

#calculate Otsu threshold for gene, autothresholdr must use integers so multiple out all decimal places then divide back down

OtsuThresh = function(seurat_object, gene_name) {

     library(autothresholdr)

     array=as.integer(seurat_object@assays$RNA@data[gene_name,]*10000000)

     auto_thresh(array, method='Otsu')[1]/10000000

 }

UTC EMCS Room capacities

EMCS 201/101, Card Auditorium, 141 students

EMCS 301, 302, lecture, 50 students

ECS 404 has 58 computers

ECS 423 has 48 computers

UTC graduate tuition, fall 2023

tuition for a Ph.D. student at CSE, UTC

I entered a total of $7,302 for your tuition waiver which includes 9 credit hours ($5,394), 9 differential hours ($540), 3 online hours ($156) and $1,200 for fall insurance. 

Faculty SSB Training through UTC Learn (Canvas)

 

Hello XXX

I'm including the instructions below:

2.Complete Faculty SSB Training through UTC Learn (Canvas)
Log into Canvas or select the ‘View in Canvas’ link once you have logged into MyMocsNet.
Select course ‘Records Training Class’.
Select Faculty SSB training module.  At the end of the training, you will need to score 80% or higher on the quiz.  If you will also be advising students, please take the Faculty SSB training with Advising quiz.

3.Complete the Training Verification Form
Once all trainings have been completed, a Training Verification Form for Faculty and Staff will need to be submitted.  You will need to enter the following:

Supervisor’s information
Your Department
 
Select ‘Next’
This form will enter a workflow and go to your supervisor for approval.  Once your supervisor approves the form, it will route to Registrar Training where your training is verified and documented. Lastly, it will route to the Faculty Records Specialist.
Thank you
Joanne

Quantum computing lec 3, complex vector space, part 2

zoom recording. 

book chapter 3.  review hilbert space and inner product, represented by trianle brackets < >

conjugate transpose, also known as Hermitian transpose,  Hermitian conjugate, adjoint matrix or transjugate, see Conjugate transpose - Wikipedia

show github

https://github.com/hongqin/quantum_sandbox/blob/main/complex_numbers.ipynb 

There is a discussion on visualization of the tensor product, and how to interpret the visualziation. 

ket and bra symbol in quantum computing

classical computing, a Boolean circuit is a mathematical mode

 In classical computing, a Boolean circuit is a mathematical model used to describe logical operations. It consists of gates, inputs, and outputs, all of which can have the value of 0 or 1 (true or false). Let's explore some common gates and their meanings in a classical Boolean circuit:
1. **AND Gate** (Symbol: ∧):
   - Description: Outputs true (1) if and only if both inputs are true.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   0
     0 | 1 |   0
     1 | 0 |   0
     1 | 1 |   1
     ```
2. **OR Gate** (Symbol: ∨):
   - Description: Outputs true (1) if at least one of the inputs is true.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   0
     0 | 1 |   1
     1 | 0 |   1
     1 | 1 |   1
     ```
3. **NOT Gate** (Symbol: ¬):
   - Description: Outputs the opposite value of the input (inverts the input).
   - Truth Table:
     ```
     A | Output
     0 |   1
     1 |   0
     ```
4. **NAND Gate** (Symbol: ⊼):
   - Description: Outputs false (0) if and only if both inputs are true; otherwise, it outputs true.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   1
     0 | 1 |   1
     1 | 0 |   1
     1 | 1 |   0
     ```
5. **NOR Gate** (Symbol: ⊽):
   - Description: Outputs true (0) if and only if both inputs are false.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   1
     0 | 1 |   0
     1 | 0 |   0
     1 | 1 |   0
     ```
6. **XOR Gate** (Exclusive OR) (Symbol: ⊕):
   - Description: Outputs true (1) if the inputs are different, false (0) if they are the same.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   0
     0 | 1 |   1
     1 | 0 |   1
     1 | 1 |   0
     ```
7. **XNOR Gate** (Exclusive NOR) (Symbol: ⊙):
   - Description: Outputs true (1) if the inputs are the same, false (0) if they are different.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   1
     0 | 1 |   0
     1 | 0 |   0
     1 | 1 |   1
     ```
These gates form the building blocks of classical Boolean circuits and can be combined in various ways to perform complex logical operations. They provide a foundational understanding of how digital logic and classical computation operate.

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