BAFF Promotes Th17 Cells and Aggravates Experimental Autoimmune Encephalomyelitis

BAFF, in addition to promoting B cell survival and differentiation, may affect T cells. The objective of this study was to determine the effect of BAFF on Th17 cell generation and its ramifications for the Th17 cell-driven disease, EAE.Th17 cells were increased in BAFF-Tg B6 (B6.BTg) mice and decreased in B6.Baff(-/-) mice. Th17 cells in B6.Baff(-/-) mice bearing a BAFF Tg (B6.Baff(-/-).BTg mice) were identical to those in B6.BTg mice, indicating that membrane BAFF is dispensable for Th17 cell generation as long as soluble BAFF is plentiful. In T + non-T cell criss-cross co-cultures, Th17 cell generation was greatest in cultures containing B6.BTg T cells and lowest in cultures containing B6.Baff(-/-) T cells, regardless of the source of non-T cells. In cultures containing only T cells, Th17 cell generation followed an identical pattern. CD4(+) cell expression of CD126 (IL-6R α chain) was increased in B6.BTg mice and decreased in B6.Baff(-/-) mice, and activation of STAT3 following stimulation with IL-6 + TGF-β was also greatest in B6.BTg cells and lowest in B6.Baff(-/-) cells. EAE was clinically and pathologically most severe in B6.BTg mice and least severe in B6.Baff(-/-) mice and correlated with MOG(35-55) peptide-induced Th17 cell responses.Collectively, these findings document a contribution of BAFF to pathogenic Th17 cell responses and suggest that BAFF antagonism may be efficacious in Th17 cell-driven diseases

Defining the Critical Hurdles in Cancer Immunotherapy

ABSTRACT: Scientific discoveries that provide strong evidence of antitumor effects in preclinical models often encounter significant delays before being tested in patients with cancer. While some of these delays have a scientific basis, others do not. We need to do better. Innovative strategies need to move into early stage clinical trials as quickly as it is safe, and if successful, these therapies should efficiently obtain regulatory approval and widespread clinical application. In late 2009 and 2010 the Society for Immunotherapy of Cancer (SITC), convened an "Immunotherapy Summit" with representatives from immunotherapy organizations representing Europe, Japan, China and North America to discuss collaborations to improve development and delivery of cancer immunotherapy. One of the concepts raised by SITC and defined as critical by all parties was the need to identify hurdles that impede effective translation of cancer immunotherapy. With consensus on these hurdles, international working groups could be developed to make recommendations vetted by the participating organizations. These recommendations could then be considered by regulatory bodies, governmental and private funding agencies, pharmaceutical companies and academic institutions to facilitate changes necessary to accelerate clinical translation of novel immune-based cancer therapies. The critical hurdles identified by representatives of the collaborating organizations, now organized as the World Immunotherapy Council, are presented and discussed in this report. Some of the identified hurdles impede all investigators, others hinder investigators only in certain regions or institutions or are more relevant to specific types of immunotherapy or first-in-humans studies. Each of these hurdles can significantly delay clinical translation of promising advances in immunotherapy yet be overcome to improve outcomes of patients with cancer

Defining the critical hurdles in cancer immunotherapy

Scientific discoveries that provide strong evidence of antitumor effects in preclinical models often encounter significant delays before being tested in patients with cancer. While some of these delays have a scientific basis, others do not. We need to do better. Innovative strategies need to move into early stage clinical trials as quickly as it is safe, and if successful, these therapies should efficiently obtain regulatory approval and widespread clinical application. In late 2009 and 2010 the Society for Immunotherapy of Cancer (SITC), convened an "Immunotherapy Summit" with representatives from immunotherapy organizations representing Europe, Japan, China and North America to discuss collaborations to improve development and delivery of cancer immunotherapy. One of the concepts raised by SITC and defined as critical by all parties was the need to identify hurdles that impede effective translation of cancer immunotherapy. With consensus on these hurdles, international working groups could be developed to make recommendations vetted by the participating organizations. These recommendations could then be considered by regulatory bodies, governmental and private funding agencies, pharmaceutical companies and academic institutions to facilitate changes necessary to accelerate clinical translation of novel immune-based cancer therapies. The critical hurdles identified by representatives of the collaborating organizations, now organized as the World Immunotherapy Council, are presented and discussed in this report. Some of the identified hurdles impede all investigators; others hinder investigators only in certain regions or institutions or are more relevant to specific types of immunotherapy or first-in-humans studies. Each of these hurdles can significantly delay clinical translation of promising advances in immunotherapy yet if overcome, have the potential to improve outcomes of patients with cancer

Designing Surface Morphologies and Anti - corrosion Properties of Anodized Aluminum Alloys via a Chemical Etching Method

To improve corrosion resistance performance of aluminum alloy,micro/nano - structures were formed by chemical etching and anodic oxidation methods on the surface of aluminum alloy. Then,anodic oxidation films with excellent anti-corrosion performance were obtained after being chemically modified via a self-assembly layer to improve the corrosion resistance of aluminum alloys. The surface morphologies and chemical elements of the as-prepared films were investigated by infrared spectroscopy and SEM. The values of surface roughness were measured by laser scanning confocal microscope. The hydrophilic/hydrophobic and anti-corrosion properties of the films were characterized by optical contact angle meter and electrochemical workstation. The influence of etching time on the surface morphologies and anti-corrosion performance was investigated. The results show that when the etching time is 3 min, the film owns the best corrosion resistance performance, the corrosion potential shifted 0.15 V positively, the corrosion current density decreased two orders of magnitude compared to bare aluminum alloy, and the static contact angle is 152°at its maximum which is due to the intact and suitable ratio of the micro/nano - structure of the films under this preparation condition

Data and Software for Developing a General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecasts

&lt;p&gt;&lt;strong&gt;Readme for GCEM Data and Code&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;1&nbsp;Data&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;1.1 Observed Precipitation Data&lt;/p&gt;&lt;p&gt;1.1.1&nbsp; &nbsp; /DataSoftware/01Observed_precipitation_data/2019071612&lt;/p&gt;&lt;p&gt;Hourly precipitation from 00:00 to 12:00 UTC on July 16, 2019 for Case 1&nbsp;&lt;/p&gt;&lt;p&gt;&lt;i&gt;surfr01h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr02h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr03h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr04h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr05h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr06h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr07h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr08h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr09h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr10h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr11h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;surfr12h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;1.1.2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; /DataSoftware/01Observed_precipitation_data/2020061312&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;/p&gt;&lt;p&gt;Hourly precipitation from 00:00 to 12:00 UTC on June 13,2020 for Case 2&nbsp;&lt;/p&gt;&lt;p&gt;&lt;i&gt;surfr01h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr02h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr03h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr04h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr05h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr06h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr07h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr08h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr09h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr10h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; surfr11h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;surfr12h.nc&lt;/i&gt;&lt;/p&gt;&lt;p&gt;1.2 Forecasted&nbsp;Precipitation Data&lt;/p&gt;&lt;p&gt;1.2.1&nbsp; &nbsp; /DataSoftware/02Forecasted_precipitation_data/2019071612&lt;/p&gt;&lt;p&gt;Data for Case 1 during 00:00-12:00 UTC on July 16, 2019&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &lt;i&gt;WRF3.2019071600000.nc&lt;/i&gt; (initial field at 12:00 UTC on July 16, 2019)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &lt;i&gt;WRF3.2019071600012.nc&lt;/i&gt; (12-hour accumulated precipitation during 00:00–12:00 UTC on July 16, 2019)&nbsp;&lt;/p&gt;&lt;p&gt;1.2.2&nbsp; &nbsp;&nbsp;/DataSoftware/02Forecasted_precipitation_data/2020061312&lt;/p&gt;&lt;p&gt;Data for Case 2 during 00:00-12:00 UTC on June 13,2020&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &lt;i&gt;WRF3.2020061300000.nc&lt;/i&gt; (initial field at 12:00 UTC on June 13,2020)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; WRF3.2020061300012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on June 13,2020)&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;2Code and Configuration Files&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;2.1 GCEM of grid precipitation forecast data (/DataSoftware/03Software_Configuration_Results_of_GCEM)&lt;/p&gt;&lt;p&gt;&lt;i&gt;pastonc6hd2.f90&lt;/i&gt;&nbsp; &nbsp;Main program, reads&nbsp;observed and forecasted precipitation data, performs GCEM verification, and outputs result files.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;module_skinput.f90&nbsp;&lt;/i&gt; &nbsp;Subprogram, module for reading one or more&nbsp;observed precipitation grid file&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; module_ybinput.f90&nbsp;&lt;/i&gt;&nbsp;&nbsp;Subprogram, module for&nbsp;reading the start (or end) forecasted precipitation grid file&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;mod_uxpasid2.f90&lt;/i&gt;&nbsp; &nbsp;Subprogram, module for used to perform GCEM verification on forecasted data&lt;/p&gt;&lt;p&gt;&nbsp; &lt;i&gt;module_outnc.f90&nbsp;&lt;/i&gt;&nbsp; Subprogram, module for outputting the verification results in netCDF file format&lt;/p&gt;&lt;p&gt;&nbsp; &lt;i&gt;compilePAS10mmd2.sh&lt;/i&gt;&nbsp; &nbsp;Used to compile source files to generate executable file under Linux&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp;r12hfile.txt&lt;/i&gt;&nbsp; Configuration file, used to specify the latitude and longitude range for data source and verification&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp;pastonc6hd2.exe&nbsp;&lt;/i&gt;&nbsp; Executable file&lt;/p&gt;&lt;p&gt;2.2 TS of grid precipitation forecast data (/DataSoftware/04Software_Configuration_Results_of_TS-Score)&lt;/p&gt;&lt;p&gt;&lt;i&gt;tsmain01.f90&lt;/i&gt;&nbsp; &nbsp;Main program, reads&nbsp;observed and forecasted precipitation data, performs TS verification, and outputs result files.&lt;/p&gt;&lt;p&gt;&nbsp; &lt;i&gt;module_skinput.f90&lt;/i&gt;&nbsp; &nbsp;Subprogram, module for reading one or more&nbsp;observed precipitation grid file&nbsp; &lt;i&gt;module_ybinput.f90&lt;/i&gt;&nbsp; &nbsp;Subprogram, module for reading the start (or end) forecasted precipitation grid file&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; module_uxtsiTure.f90&nbsp;&lt;/i&gt;&nbsp; Subprogram, module for used to perform TS verification on forecasted data&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;compileTS.sh&lt;/i&gt;&nbsp; &nbsp;Used to compile source files to generate executable file under Linux&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;r12hfile.txt&nbsp;&lt;/i&gt;&nbsp;&nbsp;Configuration file, used to specify the latitude and longitude range for data source and verification&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;tsmain01.exe&lt;/i&gt;&nbsp; &nbsp;Executable file&lt;/p&gt;&lt;p&gt;2.3 PAS mini-program (/DataSoftware/05Software_of_PAS)&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;pas10ux.f90&nbsp;&lt;/i&gt; &nbsp;Main program, used to perform PAS verification on single point precipitation forecast&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; mod_uxpasid2.f90&nbsp;&lt;/i&gt;&nbsp;&nbsp;Subprogram, Module for PAS of single point forecast&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp; &lt;i&gt;compilePAS10ux.sh&lt;/i&gt;&nbsp; &nbsp;Used to compile source files to generate executable file under Linux&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;pas10ux.exe&lt;/i&gt;&nbsp; &nbsp;Executable file&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;3 Output files&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;3.1 GCEM verification results (/DataSoftware/03Software_Configuration_Results_of_GCEM/Results_GCEM)&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;rainverd2019071612012.nc&lt;/i&gt;&nbsp;&nbsp;Result file in netCDF format&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;rainverd2019071612012.nc.txt&nbsp;&nbsp;&lt;/i&gt; Result explanation file in netCDF format&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;rainverd2020061312012.nc&lt;/i&gt;&nbsp; &nbsp;Result file in netCDF format&nbsp;&lt;/p&gt;&lt;p&gt;&lt;i&gt;&nbsp; &nbsp;rainverd2020061312012.nc.txt&nbsp;&lt;/i&gt;&nbsp; Result explanation file in netCDF format&nbsp;&lt;/p&gt;&lt;p&gt;3.2 TS verification results (/DataSoftware/04Software_Configuration_Results_of_TS-Score/Results_TS)&lt;/p&gt;&lt;p&gt;&nbsp;&lt;i&gt;&nbsp;ts12h2019071612.txt&lt;/i&gt;&nbsp; &nbsp;TS result file in text format&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&lt;i&gt; ts12h2020061312.txt&lt;/i&gt; &nbsp;&nbsp;TS result file in text format&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;4 Compiling Environment&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;The verification program runs in a UNIX environment and requires the intel compiler (v2017) and the netCDF (v4.6.1) support library&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;UNIX Environment Settings&lt;/p&gt;&lt;p&gt;# .bashrc&lt;/p&gt;&lt;p&gt;module load intel/intel-compiler-2017.5.239&lt;/p&gt;&lt;p&gt;module load intelmpi/2019.6.154&lt;/p&gt;&lt;p&gt;export F90=ifort&lt;/p&gt;&lt;p&gt;export NETCDF=/public/software/mathlib/netcdf/4.6.1_intel-2017_mpi-2017_hdf5-1.8.20-intel2017&lt;/p&gt;&lt;p&gt;export NETCDF_LIB=NETCDF/lib</p><p>export NETCDF_INC=NETCDF/include&lt;/p&gt;&lt;p&gt;export PATH=$NETCDF/bin:$PATH&lt;/p&gt;&lt;p&gt;export LD_LIBRARY_PATH=$NETCDF/lib:$LD_LIBRARY_PATH&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;5 Compiling and Running Steps&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;5.1 The steps for case 1 during 00:00–12:00 UTC on July 16, 2019&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;1.&nbsp;Creating an installation and running&nbsp;sub-directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; mkdir p2019&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;2. Copying data sources, code files and configuration files to this directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;3. Running in this directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; ./compilePAS10mmd2.sh &nbsp;&nbsp;&nbsp;&nbsp; Compile to generate executable file (pastonc6hd2.exe)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ./compileTS.sh&nbsp; &nbsp;Compile to generate executable file (tsmain01.exe)&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;4. Modifying the configuration file (r12hfile.txt)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Mainly modifying the data source path for lines 4, 19, and 23&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;5. Run the executable files&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; ./pastonc6hd2.exe &gt; outnc12hd22019071612.txt&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Creating the GCEM result file (rainverd2019071612012.nc), procedure file (outnc12hd22019071612.txt)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ./tsmain01.exe &gt;ts12h2019071612.txt&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Creating the TS result and procedure file (ts12h2019071612.txt)&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;5.2 The steps for case 2 during 00:00–12:00 UTC on June 13,2020&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;1. Creating an installation and running sub-directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; mkdir p2020&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;2. Copying data sources, code files and configuration files to this directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;3. Running in this directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; ./compilePAS10mmd2.sh&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Compile to generate executable file (pastonc6hd2.exe)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ./compileTS.sh&nbsp; &nbsp;Compile to generate executable file (tsmain01.exe)&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;4. Modifying the configuration file (r12hfile.txt)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;Mainly modifying the data source path for lines 4, 19, and 23&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;5. Run the executable files&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; ./pastonc6hd2.exe &gt; outnc12hd22020061312.txt&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Creating the GCEM result file (rainverd2020061312012.nc), procedure file (outnc12hd22020061312.txt)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ./tsmain01.exe &gt;ts12h2020061312.txt&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Creating the TS result and procedure file (ts12h2019071612.txt)&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;5.3 The steps for PAS mini-program&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;1. Creating an installation and running sub-directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; mkdir pas&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;2. Copying code files and configuration files to this directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;3. Running in this directory&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; ./compilePAS10ux.sh &nbsp;&nbsp;&nbsp; Compile to generate executable file (pastonc6hd2.exe)&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;4. linking the executable file as&nbsp;pas&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; ln -sf pas10ux.exe pas&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;5. Running the PAS mini-program&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; for example:&nbsp;./pas 15 20&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Parameter&nbsp;1:&nbsp; 15&nbsp; &nbsp;represents&nbsp;observed precipitation&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Parameter 2:&nbsp; 20&nbsp; &nbsp;represents&nbsp;forecasted precipitation&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Output: 0.895&nbsp; &nbsp;1&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; The following instructions for specific usage:&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;/strong&gt; pas &lt;i&gt;rainsk rainyb [level]&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&nbsp; &nbsp;Input&nbsp;parameters&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; Parameter 1 (&lt;i&gt;rainsk&lt;/i&gt;): observed precipitation (mm)&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; Parameter 2 (&lt;i&gt;rainyb&lt;/i&gt;): forecasted precipitation (mm)&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; Parameter 3 (&lt;i&gt;level&lt;/i&gt;):Specifing magnitude (Optional, default to&nbsp;≥0.1mm)&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&nbsp; &nbsp;Onput parameters&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Parameter 1 (&lt;i&gt;ipas&lt;/i&gt;): Pas score value (0-1) or correct value of no precipitation forecast (1);&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -999.000 represents default.&lt;/p&gt;&lt;p&gt;Parameter 2 (&lt;i&gt;iTure&lt;/i&gt;):&lt;/p&gt;&lt;p&gt;0 indicates that the rating is correct for a no precipitation forecast;&lt;/p&gt;&lt;p&gt;1 indicates a PAS score of&nbsp;≥ the specified magnitude;&lt;/p&gt;&lt;p&gt;9 indicates that it is not in the no precipitation test, nor is it the verification the specified magnitude;&lt;/p&gt;&lt;p&gt;-999 indicates default.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;6Module code main interface description&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;6.1 skinput()&lt;/p&gt;&lt;p&gt;subroutine skinput(skfile,skfilenum,rain,gridskx,gridsky,longitude,latitude)&lt;/p&gt;&lt;p&gt;&nbsp;integer,intent(in) :: skfilenum&lt;/p&gt;&lt;p&gt;&nbsp;character(len=200),dimension(skfilenum),intent(in) :: skfile&lt;/p&gt;&lt;p&gt;&nbsp;real,dimension(:,:),allocatable,intent(out) :: rain&lt;/p&gt;&lt;p&gt;&nbsp;integer,intent(out) :: gridskx,gridsky&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;usage: Read a set of observed precipitation data files and output grid&nbsp;accumulated precipitation&lt;/p&gt;&lt;p&gt;skfile,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A set of filenames that are arrays of strings (input)&lt;/p&gt;&lt;p&gt;skfilenum,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Number of files (input)&lt;/p&gt;&lt;p&gt;rain,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Accumulated precipitation,&nbsp;rain(nx,ny)&nbsp;(output)&lt;/p&gt;&lt;p&gt;gridskx,&nbsp;&nbsp; grid points, nx (output)&lt;/p&gt;&lt;p&gt;gridsky,&nbsp;&nbsp;&nbsp; grid points, ny (output)&lt;/p&gt;&lt;p&gt;gridlon,&nbsp;&nbsp; Longitude array, gridlon(nx) (output)&lt;/p&gt;&lt;p&gt;gridlat,&nbsp;&nbsp;&nbsp; Latitude array, gridlat(ny) (output)&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;6.2 ybinput()&lt;/p&gt;&lt;p&gt;subroutine ybinput(ybfile,apcp,gridybx,gridyby,gridyblon,gridyblat)&lt;/p&gt;&lt;p&gt;&nbsp; character(len=200),intent(in) :: ybfile&lt;/p&gt;&lt;p&gt;&nbsp; real,dimension(:,:),allocatable,intent(out) :: apcp,gridyblat,gridyblon&lt;/p&gt;&lt;p&gt;&nbsp; integer,intent(out) :: gridybx,gridyby&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;usage: Read a set of forecasted precipitation data files and output forecast grid precipitation&lt;/p&gt;&lt;p&gt;ybfile,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Forecast file (input)&lt;/p&gt;&lt;p&gt;apcp,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; forecasted precipitation array,&nbsp;apcp(nx, ny)&nbsp;(output)&lt;/p&gt;&lt;p&gt;gridybx,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Number of grid points for forecast data, nx&nbsp;(output)&lt;/p&gt;&lt;p&gt;gridyby,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Number of grid points for forecast data, ny&nbsp;(output)&lt;/p&gt;&lt;p&gt;gridyblon,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Longitude of forecast data, gridyblon(nx, ny)&nbsp;(output)&lt;/p&gt;&lt;p&gt;gridyblat,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Latitude of forecast data, gridyblat(nx, ny)&nbsp;(output)&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;6.3 uxpasid2()&lt;/p&gt;&lt;p&gt;subroutine uxpasid2(ui,xi,level,pas,iTure,iclass,ieps)&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp; real,intent(in)&nbsp; ::&nbsp;ui,xi,level&lt;/p&gt;&lt;p&gt;&nbsp; real,intent(out) :: pas,ieps&lt;/p&gt;&lt;p&gt;&nbsp; integer,intent(out) :: iTure,iclass&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;usage: Read in the observed and forecasted precipitation, and output the PAS score result&lt;/p&gt;&lt;p&gt;rainsk,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Observed precipitation (input)&lt;/p&gt;&lt;p&gt;rainyb,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Forecasted precipitation (input)&lt;/p&gt;&lt;p&gt;level,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Specifing magnitude (input)&lt;/p&gt;&lt;p&gt;ipas,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pas score value (0-1) or correct value of no precipitation forecast (1)&lt;/p&gt;&lt;p&gt;iTure,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0 indicates that the rating is correct for a no precipitation forecast;&lt;/p&gt;&lt;p&gt;1 indicates a PAS score of&nbsp;≥ the specified magnitude;&lt;/p&gt;&lt;p&gt;9 indicates that it is not in the no precipitation test, nor is it the verification the specified magnitude;&lt;/p&gt;&lt;p&gt;-999 indicates default.&lt;/p&gt;&lt;p&gt;iclass,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0 indicates the category (no precipitation forecast is correct)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;indicates the category of insufficient precipitation forecast (observation&nbsp;u&lt;10mm)&lt;/p&gt;&lt;p&gt;2 indicates the category of excessive(or equal) precipitation forecast(observation&nbsp;u&lt;10mm)&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3 indicates the category of insufficient precipitation forecast (observation&nbsp;u≥10mm)&lt;/p&gt;&lt;p&gt;4 indicates the category of excessive(or equal) precipitation forecast(observation&nbsp;u≥10mm)&lt;/p&gt;&lt;p&gt;-999 indicates default.&lt;/p&gt;&lt;p&gt;ieps,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0 indicates&nbsp;the forecasted and observed precipitation&nbsp;are equal&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &lt;0 indicates insufficient precipitation forecast&lt;/p&gt;&lt;p&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &gt;0 indicates excessive precipitation forecast&lt;/p&gt;&lt;p&gt;-999 indicates default.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;6.4 outpasnc()&lt;/p&gt;&lt;p&gt;subroutine outpasnc(title,vtime,vhour,gridncx,gridncy,gridnclon,gridnclat,rainncsk,rainncyb,&amp;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;pasc,pas01,pas10,pas25,pas50, pas2p5,pas5,pas15, pascnc,pasnc01,pasnc10,&amp;&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; pasnc25,pasnc50,pasnc2p5,pasnc5,pasnc15,ipsnc,epsnc,iepsnc,ips,eps,ieps)&lt;/p&gt;&lt;p&gt;&nbsp; character(len=10),intent(in) :: vtime,title&lt;/p&gt;&lt;p&gt;&nbsp; integer,intent(in) :: vhour,gridncx,gridncy&lt;/p&gt;&lt;p&gt;&nbsp; character(len=10) :: chour&lt;/p&gt;&lt;p&gt;&nbsp; real,dimension(gridncx),intent(in) :: gridnclon&lt;/p&gt;&lt;p&gt;&nbsp; real,dimension(gridncy),intent(in) :: gridn

Table_1_Association between immune-related adverse events and the efficacy of PD-1 inhibitors in advanced esophageal cancer.docx

IntroductionRecent developments in immune checkpoint inhibitors (ICIs) have improved the treatment outcomes of esophageal cancer (EC); however, it may initiate immune-related adverse events (irAEs) in some patients. The ICIs’ therapeutic efficacy is associated with irAEs in patients with non-small cell lung cancer or renal cell carcinoma, although this association is unknown in EC. The purpose of this study was to explore the association between irAEs and the efficacy of programmed death 1 (PD-1) inhibitors in EC patients.Patients and methodsThis study included patients with advanced EC treated with PD-1 inhibitors. The patients were divided into two groups according to the occurrence of irAEs. Afterward, the efficacy was compared between the irAE-negative and irAE-positive groups, and we analyzed the predictive factors of irAEs and survival.ResultsOverall, 295 patients were included in this study. Baseline characteristics were balanced in the irAE-negative and irAE-positive groups. In total, 143 (48.47%) patients experienced irAEs. The most frequent irAEs were anemia (49, 16.61%), hyperthyroidism (45, 15.25%), and pneumonitis (44, 14.92%). In total, 33 (11.19%) patients had grade ≥ 3 irAEs and pneumonitis have 15 (5.08%). No grade 5 adverse events were observed. A total of 52 (17.63%) and 91 (30.85%) patients had single and multiple irAEs, respectively. Compared with patients without irAEs, those with irAEs had significantly higher objective response rate (ORR) (37.76% vs. 25.00%, p = 0.018) and disease control rate (DCR) (92.31% vs. 83.55%, p = 0.022). Univariate Cox analyses indicated the significant association between irAEs and improved median progression-free survival (PFS) (10.27 vs. 6.2 months, p 8, radiation, as well as antiangiogenic therapy were strongly associated with irAEs development (p ConclusionIn advanced EC, patients with irAEs showed markedly better efficacy in ORR, DCR, PFS, and OS compared with patients without irAEs.</p

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer

Abstract Background Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. Methods ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. Results 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P < 0.001). The multivariate cox analysis revealed ESCC-PS could act as an independent predictor of survival from PD-1 inhibitors (P < 0.001). A combined signature incorporating ESCC-PS and expression of PD-L1 shows significantly improved accuracy in outcome prediction of PD-1 inhibitors compared to ESCC-PS and PD-L1 anlone, with the area under curve value of 0.904, 0.924, 0.610 for 6-month PFS and C-index of 0.814, 0.806, 0.601, respectively. Conclusions The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients

Simulation Studies Provide Evidence of Aerosol Transmission of SARS-CoV-2 in a Multi-Story Building via Air Supply, Exhaust and Sanitary Pipelines

A cross-layer non-vertical transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in a quarantine hotel in Guangzhou, Guangdong Province, China in June 2021. To explore the cross-layer transmission path and influencing factors of viral aerosol, we set up different scenarios to carry out simulation experiments. The results showed that the air in the polluted room can enter the corridor by opening the door to take food and move out the garbage, then mix with the fresh air taken from the outside as part of the air supply of the central air conditioning system and re-enter into different rooms on the same floor leading to the same-layer transmission. In addition, flushing the toilet after defecation and urination will produce viral aerosol that pollutes rooms on different floors through the exhaust system and the vertical drainage pipe in the bathroom, resulting in cross-layer vertical transmission, also aggravating the transmission in different rooms on the same floor after mixing with the air of the room and entering the corridor to become part of the air supply, and meanwhile, continuing to increase the cross-layer transmission through the vertical drainage pipe. Therefore, the air conditioning and ventilation system of the quarantine hotel should be operated in full fresh air mode and close the return air; the exhaust volume of the bathroom should be greater than the fresh air volume. The exhaust pipe of the bathroom should be independently set and cannot be interconnected or connected in series. The riser of the sewage and drainage pipeline of the bathroom should maintain vertical to exhaust independently and cannot be arbitrarily changed to horizontal pipe assembly

Additional file 1 of From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer

Additional file 1: Table S1. Univariate and multivariate cox regression analysis of ESCC-PS and clinicopathological characteristics for progression-free survival in training cohort. Table S2. Univariate and multivariate cox regression analysis of ESCC-PS and clinicopathological characteristics for overall survival in training cohort