How to select multiple contain conditions in a table?

Hi, I'm basically trying to filter my results to remove irrelevant rows based on two string conditions. I've attached the xslx file. I need to know the specific cause when the operating mode is 'critical' and the LOOP category is 'WR'. Basically isolating all the rows in the table where my two conditions are met.
Data = readtable('LOOP Events.xlsx','Range','D2:K370'); % Load excel from specified sheet
%LOOPS = cat(2,Data(:,1),Data(:,2),Data(:,3));
LOOPS = cat(2,table2array(Data(:,1)),table2array(Data(:,7)));%,table2array(Data(:,8)));
LOOPS = LOOPS(~all(cellfun(@isempty,LOOPS),2),:);
substr = {'Critical','SEE','EEE'};
selectedcol = contains(LOOPS,substr);
Cause = Data(:,3);
Select_Cause= Cause(selectedcol,:)

 Accepted Answer

The MATLAB approach:
T = readtable('LOOP Events.xlsx','Range','D:K');
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
T = rmmissing(T) % remove empty rows
T = 186×8 table
OperatingMode LOOPCategory LOOPClass SwitchyardRestorationTime PotentialBusRecoveryTime ActualBusRestorationTime Cause SpecificCause _____________ ____________ ______________ _________________________ ________________________ ________________________ _________ _______________ {'Critical'} {'SC'} {'LOOP-IE-I' } 15 28 28 {'HES' } {'Maintenance'} {'Critical'} {'SC'} {'LOOP-IE-I' } 0 4 4 {'Equip'} {'Breaker' } {'Shutdown'} {'SC'} {'LOOP-SD' } 15 28 28 {'HES' } {'Maintenance'} {'Shutdown'} {'SC'} {'LOOP-SD' } 77 82 82 {'Equip'} {'Other' } {'Shutdown'} {'SC'} {'LOOP-SD' } 62 63 63 {'Equip'} {'Transformer'} {'Critical'} {'SC'} {'LOOP-IE-I' } 95 118 213 {'Equip'} {'Breaker' } {'Shutdown'} {'WR'} {'LOOP-SD' } 528 533 533 {'SEE' } {'High Winds' } {'Critical'} {'SC'} {'LOOP-IE-I' } 1 2 3097 {'Equip'} {'Relay' } {'Critical'} {'WR'} {'LOOP-IE-I' } 1 16 7414 {'EEE' } {'Tornado' } {'Critical'} {'WR'} {'LOOP-IE-I' } 1 16 7414 {'EEE' } {'Tornado' } {'Shutdown'} {'SC'} {'LOOP-SD' } 39 44 44 {'Equip'} {'Transformer'} {'Critical'} {'WR'} {'LOOP-IE-I' } 1 16 7414 {'EEE' } {'Tornado' } {'Critical'} {'SC'} {'LOOP-IE-I' } 86 91 101 {'Equip'} {'Relay' } {'Critical'} {'PC'} {'LOOP-IE-NC'} 60 62 781 {'HE' } {'Switching' } {'Shutdown'} {'WR'} {'LOOP-SD' } 1120 1125 1508 {'SEE' } {'Salt Spray' } {'Shutdown'} {'SC'} {'LOOP-SD' } 15 30 136 {'HES' } {'Testing' }
X = matches(T.OperatingMode,'Critical') & matches(T.Cause,{'SEE','HEE'});
Select_Cause = T.LOOPClass(X)
Select_Cause = 19×1 cell array
{'LOOP-IE-NC'} {'LOOP-IE-NC'} {'LOOP-IE-NC'} {'LOOP-IE-NC'} {'LOOP-IE-NC'} {'LOOP-IE-I' } {'LOOP-IE-NC'} {'LOOP-IE-NC'} {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' } {'LOOP-IE-I' }

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R2022b

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on 13 Jun 2023

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on 14 Jun 2023

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