Repository: kermitt2/grobid_client_python (language: python)

Issue:
Prepare for usage as package
Like described in #14, in its current state, the repository cannot be imported due to dashes here and there.
In addition to the small changes here, the repository name would need to be changed to contain underscores instead of dashes.

Since its easy to miss in the github view, this PR:
- [x] renames grobid-client.py to grobid_client.py
- [x] changes the import in grobid_client.py to include the package name
- [x] adds an empty init file
- [x] Renames the reposity to an underscore version (I cannot propose this)

EDIT: This seems to be related to #4, which would probably work after this PR.


Discussion / hints:

Fix patch:
diff --git a/Readme.md b/Readme.md
index 5ceb539..aa67926 100644
--- a/Readme.md
+++ b/Readme.md
@@ -1,6 +1,6 @@
 # Simple python client for GROBID REST services
 
-This Python client can be used to process in an efficient concurrent manner a set of PDF in a given directory by the [GROBID](https://github.com/kermitt2/grobid) service. Results are written in a given output directory and include the resulting XML TEI representation of the PDF. 
+This Python client can be used to process in an efficient concurrent manner a set of PDF in a given directory by the [GROBID](https://github.com/kermitt2/grobid) service. It includes a command line for processing PDF on a file system and write results in a given output directory and a library for import in other python scripts. 
 
 ## Build and run
 
@@ -8,22 +8,22 @@ You need first to install and start the *grobid* service, latest stable version,
 
 ## Requirements
 
-This client has been developed and tested with Python 3.5.
+This client has been developed and tested with Python `3.5` and should work with any higher `3.*` versions. It does not require any dependencies beyond the standard python ones.
 
 ## Install
 
 Get the github repo:
 
-> git clone https://github.com/kermitt2/grobid-client-python
+> git clone https://github.com/kermitt2/grobid_client_python
 
-> cd grobid-client-python
+> cd grobid_client_python
 
 There is nothing more to do to start using the python command lines, see the next section. 
 
 ## Usage and options
 
 ```
-usage: grobid-client.py [-h] [--input INPUT] [--output OUTPUT]
+usage: grobid_client.py [-h] [--input INPUT] [--output OUTPUT]
                         [--config CONFIG] [--n N] [--generateIDs]
                         [--consolidate_header] [--consolidate_citations]
                         [--force]
@@ -58,13 +58,13 @@ optional arguments:
 
 Examples:
 
-> python3 grobid-client.py --input ~/tmp/in2 --output ~/tmp/out processFulltextDocument
+> python3 grobid_client.py --input ~/tmp/in2 --output ~/tmp/out processFulltextDocument
 
 This command will process all the PDF files present under the input directory recursively (files with extension `.pdf` only) with the `processFulltextDocument` service of GROBID, and write the resulting XML TEI files under the output directory, reusing the file name with a different file extension (`.tei.xml`), using the default `10` concurrent workers. 
 
 If `--output` is omitted, the resulting XML TEI documents will be produced alongside the PDF in the `--input` directory.
 
-> python3 grobid-client.py --input ~/tmp/in2 --output ~/tmp/out --n 20 processHeaderDocument
+> python3 grobid_client.py --input ~/tmp/in2 --output ~/tmp/out --n 20 processHeaderDocument
 
 This command will process all the PDF files present in the input directory (files with extension `.pdf` only) with the `processHeaderDocument` service of GROBID, and write the resulting XML TEI files under the output directory, reusing the file name with a different file extension (`.tei.xml`), using `20` concurrent workers. 
 
@@ -72,18 +72,29 @@ By default if an existing `.tei.xml` file is present in the output directory cor
 
 The file `test.py` gives an example of usage from a another python script. 
 
+## Using the client in your python
+
+Import and call the client as follow:
+
+```
+import grobid_client as grobid
+
+client = grobid.grobid_client(config_path="./config.json")
+client.process("processFulltextDocument", "/mnt/data/covid/pdfs", n=20)
+```
+
 ## Benchmarking
 
 Full text processing of __136 PDF__ (total 3443 pages, in average 25 pages per PDF) on Intel Core i7-4790K CPU 4.00GHz, 4 cores (8 threads), 16GB memory, `n` being the concurrency parameter:
 
 | n  | runtime (s)| s/PDF | PDF/s |
 |----|------------|-------|-------|
-| 1  | 209.0 | 1.54       | 0.65 |
-| 2  | 112.0 | 0.82       | 1.21 |
-| 3  | 80.4  | 0.59       | 1.69 |
-| 5  | 62.9  | 0.46       | 2.16 |
-| 8  | 55.7  | 0.41       | 2.44 |
-| 10 | 55.3  | 0.40       | 2.45 |
+| 1  | 209.0      | 1.54  | 0.65  |
+| 2  | 112.0      | 0.82  | 1.21  |
+| 3  | 80.4       | 0.59  | 1.69  |
+| 5  | 62.9       | 0.46  | 2.16  |
+| 8  | 55.7       | 0.41  | 2.44  |
+| 10 | 55.3       | 0.40  | 2.45  |
 
 ![Runtime Plot](resources/20180928112135.png)
 
@@ -91,7 +102,7 @@ As complementary info, GROBID processing of header of the 136 PDF and with `n=10
 
 ## Todo
 
-Benchmarking with many more files (e.g. million ISTEX PDF). Also implement existing GROBID services for text input (date, name, affiliation/address, raw bibliographical references, etc.). Better support for parameters (including elements where to put coordinates).
+Benchmarking with many more files (e.g. million PDFs). Also implement existing GROBID services for text input (date, name, affiliation/address, raw bibliographical references, etc.). Better support for parameters.
 
 ## License and contact
 
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/grobid-client.py b/grobid_client.py
similarity index 79%
rename from grobid-client.py
rename to grobid_client.py
index 59d44ea..dea4b05 100644
--- a/grobid-client.py
+++ b/grobid_client.py
@@ -14,8 +14,9 @@
 Given the limits of ThreadPoolExecutor (input stored in memory, blocking Executor.map until the whole input
 is acquired), it works with batches of PDF of a size indicated in the config.json file (default is 1000 entries). 
 We are moving from first batch to the second one only when the first is entirely processed - which means it is
-slightly sub-optimal, but should scale better. However acquiring a list of million of files in directories would
-require something scalable too, which is not implemented for the moment.   
+slightly sub-optimal, but should scale better. Working without batch would mean acquiring a list of million of 
+files in directories would require something scalable too (e.g. done in a separate thread), which is not 
+implemented for the moment.
 '''
 class grobid_client(ApiClient):
 
@@ -43,31 +44,44 @@ def _load_config(self, path='./config.json'):
         else:
             print("GROBID server is up and running")
 
-    def process(self, input, output, n, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates):
+    def process(self, service, input_path, 
+            output=None, 
+            n=10, 
+            generateIDs=False, 
+            consolidate_header=True, 
+            consolidate_citations=False, 
+            include_raw_citations=False, 
+            include_raw_affiliations=False, 
+            teiCoordinates=False,
+            force=True):
         batch_size_pdf = self.config['batch_size']
         pdf_files = []
 
-        for (dirpath, dirnames, filenames) in os.walk(input):
+        print(input_path)
+
+        for (dirpath, dirnames, filenames) in os.walk(input_path):
+            print(dirpath, dirnames, filenames)
             for filename in filenames:
                 if filename.endswith('.pdf') or filename.endswith('.PDF'): 
+                    print(filename)
                     pdf_files.append(os.sep.join([dirpath, filename]))
 
                     if len(pdf_files) == batch_size_pdf:
-                        self.process_batch(pdf_files, output, n, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates)
+                        self.process_batch(service, pdf_files, output, n, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, teiCoordinates, force)
                         pdf_files = []
 
         # last batch
         if len(pdf_files) > 0:
-            self.process_batch(pdf_files, output, n, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates)
+            self.process_batch(service, pdf_files, output, n, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, teiCoordinates, force)
 
-    def process_batch(self, pdf_files, output, n, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates):
+    def process_batch(self, service, pdf_files, output, n, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, teiCoordinates, force):
         print(len(pdf_files), "PDF files to process")
         #with concurrent.futures.ThreadPoolExecutor(max_workers=n) as executor:
         with concurrent.futures.ProcessPoolExecutor(max_workers=n) as executor:
             for pdf_file in pdf_files:
-                executor.submit(self.process_pdf, pdf_file, output, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates)
+                executor.submit(self.process_pdf, service, pdf_file, output, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, teiCoordinates, force)
 
-    def process_pdf(self, pdf_file, output, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates):
+    def process_pdf(self, service, pdf_file, output, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, teiCoordinates, force):
         # check if TEI file is already produced 
         # we use ntpath here to be sure it will work on Windows too
         pdf_file_name = ntpath.basename(pdf_file)
@@ -152,7 +166,6 @@ def process_pdf(self, pdf_file, output, service, generateIDs, consolidate_header
     config_path = args.config
     output_path = args.output
     
-    n =10
     if args.n is not None:
         try:
             n = int(args.n)
@@ -183,7 +196,16 @@ def process_pdf(self, pdf_file, output, service, generateIDs, consolidate_header
 
     start_time = time.time()
 
-    client.process(input_path, output_path, n, service, generateIDs, consolidate_header, consolidate_citations, include_raw_citations, include_raw_affiliations, force, teiCoordinates)
+    client.process(service, input_path, 
+            output=output_path, 
+            n=n, 
+            generateIDs=generateIDs, 
+            consolidate_header=consolidate_header, 
+            consolidate_citations=consolidate_citations, 
+            include_raw_citations=include_raw_citations, 
+            include_raw_affiliations=include_raw_affiliations, 
+            teiCoordinates=teiCoordinates,
+            force=force)
 
     runtime = round(time.time() - start_time, 3)
     print("runtime: %s seconds " % (runtime))
diff --git a/setup.py b/setup.py
index 93ef7fe..70c1ad8 100644
--- a/setup.py
+++ b/setup.py
@@ -2,9 +2,9 @@
 
 from setuptools import setup, find_packages
 
-setup(name='grobid-client-python',
-      version='0.0.1',
-      description='grobid-client-python',
+setup(name='grobid_client_python',
+      version='0.0.2',
+      description='grobid_client_python',
       author='kermitt2',
       packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
       license='LICENSE',


Test patch:
diff --git a/resources/test/0046d83a-edd6-4631-b57c-755cdcce8b7f.pdf b/resources/test/0046d83a-edd6-4631-b57c-755cdcce8b7f.pdf
new file mode 100644
index 0000000..2112f9b
Binary files /dev/null and b/resources/test/0046d83a-edd6-4631-b57c-755cdcce8b7f.pdf differ
diff --git a/resources/test/0046d83a-edd6-4631-b57c-755cdcce8b7f.tei.xml b/resources/test/0046d83a-edd6-4631-b57c-755cdcce8b7f.tei.xml
new file mode 100644
index 0000000..19af3bd
--- /dev/null
+++ b/resources/test/0046d83a-edd6-4631-b57c-755cdcce8b7f.tei.xml
@@ -0,0 +1,1866 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<TEI xml:space="preserve" xmlns="http://www.tei-c.org/ns/1.0" 
+xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
+xsi:schemaLocation="http://www.tei-c.org/ns/1.0 /home/lopez/grobid/grobid-home/schemas/xsd/Grobid.xsd"
+ xmlns:xlink="http://www.w3.org/1999/xlink">
+	<teiHeader xml:lang="en">
+		<fileDesc>
+			<titleStmt>
+				<title level="a" type="main">Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site</title>
+			</titleStmt>
+			<publicationStmt>
+				<publisher>Springer Nature</publisher>
+				<availability status="unknown"><p>Copyright Springer Nature</p>
+				</availability>
+				<date type="published" when="2016-03-08">2016-03-08</date>
+			</publicationStmt>
+			<sourceDesc>
+				<biblStruct>
+					<analytic>
+						<author>
+							<persName coords="1,56.69,234.15,85.88,9.71"><forename type="first">Cristiano</forename><surname>De Marchis</surname></persName>
+							<email>cristiano.demarchis@uniroma3.it</email>
+							<affiliation key="aff0">
+								<orgName type="department" key="dep1">Division of Functional and Restorative Neurosurgery</orgName>
+								<orgName type="department" key="dep2">Department of Neurosurgery</orgName>
+								<orgName type="institution">Eberhard Karls University</orgName>
+								<address>
+									<addrLine>Otfried-Mueller-Str.45</addrLine>
+									<postCode>72076</postCode>
+									<settlement>Tübingen</settlement>
+									<country key="DE">Germany</country>
+								</address>
+							</affiliation>
+						</author>
+						<author>
+							<persName><forename type="first">Thiago</forename><surname>Santos Monteiro</surname></persName>
+						</author>
+						<author>
+							<persName coords="1,278.42,234.15,98.97,9.71"><forename type="first">Cristina</forename><surname>Simon-Martinez</surname></persName>
+							<affiliation key="aff0">
+								<orgName type="department" key="dep1">Division of Functional and Restorative Neurosurgery</orgName>
+								<orgName type="department" key="dep2">Department of Neurosurgery</orgName>
+								<orgName type="institution">Eberhard Karls University</orgName>
+								<address>
+									<addrLine>Otfried-Mueller-Str.45</addrLine>
+									<postCode>72076</postCode>
+									<settlement>Tübingen</settlement>
+									<country key="DE">Germany</country>
+								</address>
+							</affiliation>
+						</author>
+						<author>
+							<persName coords="1,391.12,234.15,60.85,9.71"><forename type="first">Silvia</forename><surname>Conforto</surname></persName>
+							<affiliation key="aff0">
+								<orgName type="department" key="dep1">Division of Functional and Restorative Neurosurgery</orgName>
+								<orgName type="department" key="dep2">Department of Neurosurgery</orgName>
+								<orgName type="institution">Eberhard Karls University</orgName>
+								<address>
+									<addrLine>Otfried-Mueller-Str.45</addrLine>
+									<postCode>72076</postCode>
+									<settlement>Tübingen</settlement>
+									<country key="DE">Germany</country>
+								</address>
+							</affiliation>
+						</author>
+						<author>
+							<persName coords="1,75.74,248.16,78.49,9.71"><forename type="first">Alireza</forename><surname>Gharabaghi</surname></persName>
+							<email>alireza.gharabaghi@uni-tuebingen.de</email>
+							<affiliation key="aff0">
+								<orgName type="department" key="dep1">Division of Functional and Restorative Neurosurgery</orgName>
+								<orgName type="department" key="dep2">Department of Neurosurgery</orgName>
+								<orgName type="institution">Eberhard Karls University</orgName>
+								<address>
+									<addrLine>Otfried-Mueller-Str.45</addrLine>
+									<postCode>72076</postCode>
+									<settlement>Tübingen</settlement>
+									<country key="DE">Germany</country>
+								</address>
+							</affiliation>
+						</author>
+						<title level="a" type="main">Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site</title>
+					</analytic>
+					<monogr>
+						<title level="j" type="main">Journal of NeuroEngineering and Rehabilitation</title>
+						<title level="j" type="abbrev">J NeuroEngineering Rehabil</title>
+						<idno type="eISSN">1743-0003</idno>
+						<imprint>
+							<publisher>Springer Nature</publisher>
+							<biblScope unit="volume">13</biblScope>
+							<biblScope unit="issue">1</biblScope>
+							<date type="published" when="2016-03-08" />
+						</imprint>
+					</monogr>
+					<idno type="DOI">10.1186/s12984-016-0129-6</idno>
+					<idno type="PMID">26955873</idno>
+					<idno type="PMCID">PMC4782521</idno>
+					<ptr type="open-access" target="https://jneuroengrehab.biomedcentral.com/track/pdf/10.1186/s12984-016-0129-6" />
+					<note type="submission">Received: 11 November 2015 Accepted: 24 February 2016</note>
+				</biblStruct>
+			</sourceDesc>
+		</fileDesc>
+		<encodingDesc>
+			<appInfo>
+				<application version="0.6.2-SNAPSHOT" ident="GROBID" when="2020-12-20T08:09+0000">
+					<desc>GROBID - A machine learning software for extracting information from scholarly documents</desc>
+					<ref target="https://github.com/kermitt2/grobid"/>
+				</application>
+			</appInfo>
+		</encodingDesc>
+		<profileDesc>
+			<textClass>
+				<keywords>
+					<term>Neuromuscular electrical stimulation</term>
+					<term>Multi-contact stimulation</term>
+					<term>EMG</term>
+					<term>M-wave</term>
+					<term>Hand function</term>
+					<term>Neurorehabilitation</term>
+				</keywords>
+			</textClass>
+			<abstract>
+<div xmlns="http://www.tei-c.org/ns/1.0"><p>Background: Functional Electrical Stimulation (FES) is increasingly applied in neurorehabilitation. Particularly, the use of electrode arrays may allow for selective muscle recruitment. However, detecting the best electrode configuration constitutes still a challenge. Methods: A multi-contact set-up with thirty electrodes was applied for combined FES and electromyography (EMG) recording of the forearm. A search procedure scanned all electrode configurations by applying single, sub-threshold stimulation pulses while recording M-waves of the extensor digitorum communis (EDC), extensor carpi radialis (ECR) and extensor carpi ulnaris (ECU) muscles. The electrode contacts with the best electrophysiological response were then selected for stimulation with FES bursts while capturing finger/wrist extension and radial/ulnar deviation with a kinematic glove. Results: The stimulation electrodes chosen on the basis of M-waves of the EDC/ECR/ECU muscles were able to effectively elicit the respective finger/wrist movements for the targeted extension and/or deviation with high specificity in two different hand postures. Conclusions: A subset of functionally relevant stimulation electrodes could be selected fast, automatic and nonpainful from a multi-contact array on the basis of muscle responses to subthreshold stimulation pulses. The selectivity of muscle recruitment predicted the kinematic pattern. This electrophysiologically driven approach would thus allow for an operator-independent positioning of the electrode array in neurorehabilitation.</p></div>
+			</abstract>
+		</profileDesc>
+	</teiHeader>
+	<facsimile>
+		<surface n="1" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="2" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="3" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="4" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="5" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="6" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="7" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="8" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+		<surface n="9" ulx="0.0" uly="0.0" lrx="595.276" lry="790.866"/>
+	</facsimile>
+	<text xml:lang="en">
+		<body>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Background</head><p>Functional Electrical Stimulation (FES) is a widely used technique for inducing muscle contraction. FES induces muscle activation through the application of currents that are able to excite the axons of the motor-neurons innervating the target muscles. This technique has been extensively studied for both training and rehabilitation purposes. A number of studies have shown the effectiveness of FES for improving muscle function in different central nervous system (CNS) disorders such as stroke <ref type="bibr" coords="1,304.72,601.94,10.01,8.98" target="#b0">[1]</ref>, tremor <ref type="bibr" coords="1,352.28,601.94,10.01,8.98" target="#b1">[2]</ref>, multiple sclerosis <ref type="bibr" coords="1,442.64,601.94,10.19,8.98" target="#b2">[3,</ref><ref type="bibr" coords="1,455.84,601.94,8.06,8.98" target="#b3">4]</ref> or spinal cord injury <ref type="bibr" coords="1,323.49,613.96,11.21,8.98" target="#b4">[5]</ref> for both the upper and lower extremity <ref type="bibr" coords="1,501.74,613.96,10.01,8.98" target="#b5">[6]</ref>.</p><p>Particularly, the hand function is a major target of FES in CNS disorders due to its relevance for quality of life <ref type="bibr" coords="1,510.50,637.94,10.89,8.98" target="#b6">[7]</ref> and activities of daily living <ref type="bibr" coords="1,401.20,649.96,10.83,8.98" target="#b7">[8]</ref> in both the acute and chronic phase <ref type="bibr" coords="1,330.78,661.92,9.61,8.98" target="#b8">[9]</ref>. In this context, FES might enhance hand functionality when physical therapy alone is ineffective <ref type="bibr" coords="1,502.82,673.94,12.28,8.98" target="#b9">[10]</ref><ref type="bibr" coords="1,515.10,673.94,4.09,8.98" target="#b10">[11]</ref><ref type="bibr" coords="1,515.10,673.94,4.09,8.98" target="#b11">[12]</ref><ref type="bibr" coords="1,515.10,673.94,4.09,8.98" target="#b12">[13]</ref><ref type="bibr" coords="1,519.19,673.94,12.28,8.98" target="#b13">[14]</ref>.</p><p>Due to its simplicity, most FES applications use pairs of electrodes in bi-or monopolar configuration <ref type="bibr" coords="2,254.16,100.72,14.60,8.98" target="#b14">[15]</ref>. Depending on the shape, size and positioning of the active and return electrodes, different current paths are induced in the motor axons underlying the stimulating electrodes, and different muscles are thus targeted. In particular, the larger the electrode, the less specific the stimulation becomes <ref type="bibr" coords="2,145.82,172.72,14.62,8.98" target="#b15">[16]</ref>. When aiming at the recovery of specific hand functions, it is though essential to induce specific muscle contraction. However, surface electrode stimulation is generally limited by its lack of selectivity. This is particularly critical when targeting specific hand movements due to the complexity of the muscle topography within the forearm. A more selective stimulation can be achieved with the use of an electrode array <ref type="bibr" coords="2,81.42,268.76,15.09,8.98" target="#b16">[17,</ref><ref type="bibr" coords="2,100.73,268.76,11.32,8.98" target="#b17">18]</ref>, which needs, however, to be adjusted in terms of contact selection for each individual subject <ref type="bibr" coords="2,56.69,292.74,14.60,8.98" target="#b18">[19]</ref>. Such an approach would facilitate the stimulation of specific muscles for selective movements, physiological synergies or more complex patterns relevant for activities of daily living.</p><p>Up till now, different procedures have been proposed to detect the optimal FES site <ref type="bibr" coords="2,198.10,352.72,12.75,8.98" target="#b19">[20]</ref><ref type="bibr" coords="2,210.85,352.72,4.25,8.98" target="#b20">[21]</ref><ref type="bibr" coords="2,210.85,352.72,4.25,8.98" target="#b21">[22]</ref><ref type="bibr" coords="2,210.85,352.72,4.25,8.98" target="#b22">[23]</ref><ref type="bibr" coords="2,210.85,352.72,4.25,8.98" target="#b23">[24]</ref><ref type="bibr" coords="2,215.11,352.72,12.75,8.98" target="#b24">[25]</ref>. All of these methods have in common that they probe stimulation induced muscle twitch responses, e.g. by capturing the kinematic response of the targeted degrees of freedom. However, this kinematic response might be diminished or even absent e.g. in stroke patients due to spasticity. Moreover, the application of high current levels could lead to pain and muscle fatigue if applied repeatedly. However, electrical stimulation pulses evoke an electrophysiological response as well, i.e. the M-wave, which closely relates to muscle fiber recruitment. This electrophysiological response is detectable even when subthreshold stimulation pulses are applied, which do not elicit kinematic responses, i.e. movements. The M-wave represents the synchronous muscle response to the action potentials traveling ortho-dromically through the axon of the motor neuron; this signal can be detected quickly and reliably by using standard electromyographic (EMG) measurements over the target muscle, thereby providing an objective and specific measure of the neuromuscular effects of electrical stimulation <ref type="bibr" coords="2,249.85,592.69,12.76,8.98" target="#b25">[26]</ref><ref type="bibr" coords="2,262.61,592.69,4.25,8.98" target="#b26">[27]</ref><ref type="bibr" coords="2,266.86,592.69,12.76,8.98" target="#b27">[28]</ref>.</p><p>The present study is based on the hypothesis that the electrophysiological response to sub-motor threshold single pulse neuromuscular electrical stimulation will predict the effects of supra-motor threshold FES bursts. The mapping procedure is based on M-wave recordings from multiple muscles of the forearm during single pulse stimulation. The functional relevance of the selected stimulation sites is verified after the mapping with stimulation bursts producing specific movements of fingers and wrist extension/deviation. This electrophysiologically driven approach is expected to lead to automatic identification of selective electrode configurations of a multi-contact array for functional hand opening. The feasibility of selective muscle recruitment and kinematic pattern prediction is tested here in healthy subjects, while the ultimate goal will be to translate this technique to neuro-rehabilitation for the recovery of hand function in stroke patients.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Methods</head><p>This section is divided into three main sub-sections. The first part describes the setup used for applying electrical stimulation, recording the EMG responses and measuring the kinematic output. The second part describes all the procedures adopted during the experimental sessions, including the stimulation protocol and data processing. The third part describes the offline analysis procedures for performance evaluation.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Participants</head><p>Eight young healthy subjects (27.4 ± 2.6 years old) participated in the study after providing written informed consent. The study was carried out according to the principles of the declaration of Helsinki and was approved by the ethics committee of the medical faculty of the University of Tuebingen. All subjects were right handed, and the stimulated forearm was the left one.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Experimental setup</head><p>Neuromuscular electrical stimulation and EMG recording FES was provided through a surface electrode array (RehaStim2+, HASOMED GmbH, Magdeburg, Germany). The array was composed of two main blocks with fixed electrode positions: a proximal block containing 15 electrodes with 9 stimulation and 6 return contacts, and a distal block containing 6 stimulation and 6 return contacts arranged in a 3×4 configuration, as shown in Fig. <ref type="figure" coords="2,512.05,496.72,3.52,8.98">1</ref>. The stimulation/return electrodes were oval shaped (gold, 1,4 cm x 0,8 cm) and had an inter-electrode, center-tocenter distance of 2 cm and 2,2 cm in the vertical and horizontal direction, respectively. The contact quality was improved through the use of a conductive gel sheet placed on each electrode. A stimulation configuration was defined as a pair composed of an active and a return electrode and will be referred to as stimulation pattern. Based on this array configuration, 180 different FES stimulation patterns could be studied.</p><p>EMG signals were recorded with a monopolar configuration;, the recording electrodes (Ag/AgCl, AMBU Neuroline) were positioned over the three targeted forearm muscles, i.e. Extensor Digitorum Communis (EDC), Extensor Carpi Radialis (ECR) and Extensor Carpi Ulnaris (ECU), while reference and ground electrodes were positioned over the olecranon. EMG electrodes were positioned based on anatomical landmarks identified in previous studies aiming at recording selective EMG activity from forearm muscles <ref type="bibr" coords="3,214.50,340.76,15.41,8.98" target="#b28">[29,</ref><ref type="bibr" coords="3,233.89,340.76,11.55,8.98" target="#b29">30]</ref>. The correct positioning was supported by palpation during targeted movements <ref type="bibr" coords="3,148.28,364.74,14.96,8.98" target="#b28">[29]</ref>. EMG signals were sampled at 5000 Hz (BrainAmp DC amplifier; Brain Products GmbH, Gilching, Germany) and digitized through a 16 bit A/D converter.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Kinematics recording</head><p>Finger and wrist movements were recorded by using a DG5 VHand 3.0 Glove device (DGTech Engineering Solutions, Italy, http://www.dg-tech.it/vhand3) providing a resolution of 12 bit for the bending sensors and of 0.01°, 0.01°and 0.05°for pitch, roll and yaw, respectively, for the attitude estimation of the hand. This devise was equipped with five resistive bending sensors that provided a measure of bending for the five fingers. A triaxial Inertial Measurement Unit placed on the dorsal side of the hand provided the pitch, yaw and roll angles, respectively, corresponding to finger and wrist flexion/ extension, radial/ulnar deviation and forearm pronation/ supination angle. Kinematic data were recorded with a sampling frequency of 100 Hz and digitized with 16 bits.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Experimental protocol</head><p>During the experimental sessions, the subjects sat comfortably in a chair with the forearm kept in a relaxed pronated position, while the wrist was kept relaxed and free of any constraints. Before the stimulation part of the experimental protocol, the subjects were asked to perform 10 natural hand openings. This template of hand openings was used as a reference for both the evaluation of the behavioral outcome of FES and for post-hoc data analysis.</p><p>The experimental protocol consisted of two main parts that will be described in two separate sections. The first part describes the single pulse scanning procedure: all possible stimulation patterns were used to identify subsets of optimal stimulation patterns for each of the three analyzed muscles. M-waves generated by the application of each pulse were then analyzed. The second part describes the application of FES bursts at the previously identified stimulation patterns with the aim of eliciting targeted movements of the wrist and fingers. The setup, including glove and electrodes donning, scanning the array and EMG offline analysis, took approximately 15 min.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Scanning the array and selecting the optimal stimulation patterns</head><p>In order to determine subsets of optimal stimulation patterns, the electrode array was scanned with single stimulation pulses. Each pulse was biphasic and applied with a fixed total pulse width of 500 μs. This pulsewidth value was chosen after a pilot study in order to reduce inter-subject variability of the threshold and stabilize the current values <ref type="bibr" coords="3,418.74,592.76,14.60,8.98" target="#b17">[18]</ref>. First, single pulses were delivered at 10 mA at each stimulation pattern. The subjects were asked to report any uncomfortable sensation. The contacts inducing any kind of discomfort were excluded. The remaining patterns were then stimulated at three different amplitudes (9 mA, 10 mA and 11 mA) with each current level applied three times at each pattern, i.e. resulting in nine pulses per pattern. These intensities were below the kinematic threshold when applied as single pulses, i.e. they did not elicit functional finger extension, wrist extension and ulnar deviation. Single pulses were applied every 300 ms in order to Fig. <ref type="figure" coords="3,78.07,303.06,4.42,7.36">1</ref> Multi-contact set-up visualized for the left arm: Electrode array for FES with a fixed configuration of two blocks, i.e. a proximal and a distal (close to the hand) block with 15 and 12 contacts respectively, and 3 flexible electrodes for EMG recording avoid summation effects and muscle contraction by allowing a complete muscle relaxation before the application of the next pulse <ref type="bibr" coords="4,163.50,112.74,14.60,8.98" target="#b30">[31]</ref>. This scanning procedure led to the application of a maximum of 1620 single pulses and was completed in approximately 8 min.</p><p>After the completion of the scanning procedure, a EMG offline analysis was performed with a custom written MATLAB script (The MathWorks, Natick, MA). After visual inspection of the data quality, EMG signals were band pass filtered (10Hz-1000Hz, 3rd order Butterworth filter) in order to remove low frequency movement artifacts and high frequency noise. When present, power line noise interference and the first two higher order harmonics were removed with a 5th order Butterworth band stop filter around 50Hz. In order to eliminate the stimulation artifact from the analyzed EMG channels, a threshold algorithm was applied to the absolute value of the EMG signal derivative, and the signal was blanked within the detected spike intervals. This procedure led to EMG signals containing only the muscular response to the applied stimulation. In order to compare the responses across different targeted muscles, each EMG signal was normalized with respect to the amplitude of the maximum absolute value of the processed EMG, corresponding to the maximum evoked M-wave among all the provided pulses and stimulation patterns.</p><p>After EMG pre-processing, the muscular response to each delivered pulse for each muscle was analyzed in a time window from 2 ms to 40 ms after the identified stimulation artifacts. The muscular response was quantified in terms of features extracted from the recorded Mwaves. The peak-to-peak amplitude was calculated for the M-waves recorded from each muscle. The selection of the optimal stimulation patterns was based on the selectivity of the recorded M-waves: for each pattern, the median Mwave was calculated from the muscle response of the nine applied pulses. A stimulation pattern was considered optimal for a muscle if the peak-to-peak of the median Mwave was at least twice as a high as the median M-wave recorded from each of the other two muscles (Fig. <ref type="figure" coords="4,259.15,556.75,3.34,8.98" target="#fig_0">2</ref>).</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Stimulating at the optimal patterns</head><p>After identifying the subset of optimal stimulation patterns for each muscle, each pattern was stimulated individually through the application of a 5 s FES burst. Each burst was applied with a frequency of 30 Hz and a pulse width of 500 μs. All identified patterns were first stimulated with the current level used during the test procedure (10 mA as default value). If no movement was present at the baseline level, the amplitude was gradually increased in steps of 2 mA until the kinematic threshold was reached and a visible movement elicited. The stimulation of the optimal EDC pattern was carried out in two different starting positions of the forearm: a reference position of the arm, identical to the one during the scanning procedure, and a 90°neutral forearm position. This procedure was carried out to study the robustness of the obtained patterns to a rotation of the forearm, during which the relative rotation of the skin on the muscles might lead to muscles sliding with respect to the original electrodes position. For both conditions, the subject was asked to close the handwithout providing resistance to the elicited movementbefore the application of each FES burst. For ECR (wrist extension) and ECU (wrist ulnar deviation) the same procedure was applied, but using only the reference forearm posture; this additional part of the experimental protocol was performed to check the sensitivity of the stimulation patterns to generic arm movements.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Data analysis</head><p>Percentage of fingers extensions with respect to template opening was used as a measure of behavioral outcome for finger extension, as indicated in the following equation:</p><formula xml:id="formula_0" coords="4,319.75,324.34,218.81,23.08">Fext i ¼ 100 FE i T FEi<label>ð1Þ</label></formula><p>Where Fext i denotes the metric for the extension of the i-th finger, FE i denotes the value measured by the glove for the extension of the i-th finger, and T FEi indicates the value measured during template hand opening for the same finger.</p><p>For the other degrees of freedom (i.e. wrist extension and ulnar deviation) absolute angles were used as measures of behavioral outcome. In order to assess the performance of the stimulated patterns, a measure of selectivity was introduced for each stimulated pattern targeting a specific degree of freedom (fingers extensions, wrist extension and ulnar deviation). Assuming that the three targeted degrees of freedom are independent, we can represent each of them as one of three orthogonal directions in a Cartesian reference system and define a measure of selectivity related to the cosine of the elevation angle in spherical coordinates as follows:</p><formula xml:id="formula_1" coords="4,319.75,568.97,218.81,25.18">S ¼ cos θ ð Þ ¼ A ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A 2 þ B 2 þ C 2 p<label>ð2Þ</label></formula><p>Where A is the targeted degree of freedom, B and C are the non-targeted degrees of freedom and θ is the angle between the desired direction and the obtained one. The parameter S provides a direct measure of selectivity for each stimulated pattern with respect to the targeted degree of freedom, assuming values in the range [0:1].</p><p>In order to evaluate the effectiveness of the proposed method, we compared the behavioral outcomes emerging from the stimulation of the optimal patterns for the targeted degree of freedom A OPT with a set of control measurements, defined as the behavioral outcome A CONTROL when the optimal patterns for B and C were applied. This procedure was adopted because it was not possible to apply effective control measurements deriving from random patterns, due to fatigue and potential pain. In order to assess the statistical significance of the difference, we performed multiple Wilcoxon tests. The difference was considered statistically significant for values of p &lt; 0.05.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Results</head><p>We explored 1440 stimulation patterns during the scanning procedure, i.e. 180 patterns in each of the 8 subjects; 50 stimulation patterns (about 3 %) were excluded from further evaluation due to discomfort. In 12 cases, the selected pattern, which was comfortable during the scanning procedure, revealed discomfort, when stimulated with 30Hz bursts at baseline intensity (5 cases for EDC, 2 cases for ECR, 5 cases for ECU). For these patterns the intensity was not increased and they were excluded from further analysis.</p><p>The presented approach was successful in detecting the stimulation patterns which were effective for functional hand opening in all subjects. This allowed reducing the total number of possible stimulation patterns (n = 180) to the sufficient ones for hand opening (meanFE = 12.5, minFE = 5, maxFE = 29), wrist extension (meanWE = 7.4, minWE = 6, maxWE = 10) and ulnar deviation (meanUD = 8.7, minUD = 3, maxUD = 15), respectively.</p><p>With regard to hand opening, the targeted template hand opening with a complete extension of all fingers could be achieved with 66 % of the selected stimulation patterns; in 21 % of the patterns, an incomplete extension of the fingers was achieved or additional wrist deviation occurred. In only 13 % of the patterns, M-waves did not predict the targeted movements.</p><p>To better assess the efficacy of stimulation for functional hand opening, the findings were divided into two classes on the basis of the functional relevance of the finger extension (i.e. functional or non-functional extension). The finger extension was defined as functionally relevant when corresponding to two thirds of the full extension from a closed finger position. The number of functionally relevant stimulation patterns was highly dependent on the subject, ranging from a minimum of 2 to a maximum of 21 across subjects (mean number of functional patterns = 8.9). In general, the functional finger extension was closely mimicking the template hand opening.</p><p>For the different muscles, a different number (N: mean/median) of 2 mA intensity increments from the baseline value were necessary to achieve the results presented in this section: N EDC = 2.25/2, N ECR = 2.71/3, N ECU = 1.25/1. ECU required generally lower currents for eliciting movements, because in the reference position ulnar deviation was the only degree of freedom that had not to overcome gravity.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Optimal patterns vs control measurements</head><p>The behavioral outcome obtained with stimulation at the optimal pattern was significantly higher than that obtained from the control measurements. More specifically, with regard to hand opening, the extension of all fingers was significantly higher with the optimal Finger Extension Patterns (FEP) as compared to the corresponding control measurements (Wrist Extension Patterns, WEP, and Ulnar Deviation Patterns, UDP) (Fig. <ref type="figure" coords="5,528.38,676.72,3.40,8.98">3</ref>). With regard to wrist extension, the WEP was significantly more effective that UDP, but not than FEP (Fig. <ref type="figure" coords="5,509.73,700.76,3.52,8.98">4</ref>, left). Moreover, ulnar deviation was significantly more effective during UDP as compared to WEP and FEP (Fig. <ref type="figure" coords="5,499.13,724.74,4.90,8.98">4</ref> right). </p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Selectivity with optimal stimulation pattern</head><p>All movements obtained by the respective optimal stimulation pattern (FEP for hand opening/finger extension, WEP for wrist extension and UDP for ulnar deviation) proved to be highly selective. When the parameter S was higher than 0.6, this indicated that the contribution of the non-targeted movements was negligible in comparison to the targeted ones (Fig. <ref type="figure" coords="6,246.10,364.74,3.40,8.98">5</ref>).</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Hand opening with a rotated forearm position</head><p>When applying the identified optimal FEP during changed forearm/hand position no differences were found with regard to the individual finger extension. The identified patterns were thus independent of the forearm pronation/supination postures, i.e. with a 90°rotation of the forearm (Fig. <ref type="figure" coords="6,128.41,465.71,3.40,8.98">6</ref>).</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Discussion</head><p>We introduced a novel FES mapping procedure with a multi-contact array. This approach allowed identifying a subset of electrodes that were optimal for eliciting functional hand opening with selective muscle recruitment for achieving the targeted movement. Furthermore, other movements, such as wrist extension and ulnar deviation, could be elicited as well with detecting a different optimal electrode configuration for selective muscle recruitment. Thereby, the presented procedure facilitated the detection of both muscle specific and behaviorally relevant electrode configurations.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Innovation of the study</head><p>With respect to previous studies aiming at identifying the optimal stimulation patterns for eliciting selective hand and wrist movements, this is the first to fully rely on an electrophysiologically-based approach. Moreover, this approach is novel in using a variable location for both the active and return electrodes; the use of a set of different return electrodes on the array led to an increased amount of available stimulation patterns. This aspect might play an important role when aiming at an operatorindependent FES positioning <ref type="bibr" coords="6,427.35,518.72,14.84,8.98" target="#b16">[17,</ref><ref type="bibr" coords="6,446.34,518.72,11.20,8.98" target="#b31">32]</ref>. Once the optimal muscle-selective stimulation patterns have been identified, further computational methods could be applied to generate even more specific movement patterns <ref type="bibr" coords="7,228.42,328.74,14.89,8.98" target="#b23">[24,</ref><ref type="bibr" coords="7,245.82,328.74,11.20,8.98" target="#b32">33]</ref>.</p><p>Analyzing the muscle response to specific stimulation patterns through the use of surface EMG allows the extraction of specific features from the signal. In this study we studied the main muscles controlling the two kinematic degrees of freedom of the wrist (flexion/extension and radial/ulnar deviation) and the metacarpal joints of the fingers. However, increasing the sources of EMG recording even further would allow a selective identification of optimal patterns for a higher number muscles and of degrees of freedom: the thumb movement, for example, was not explicitly targeted in this study; however recording EMG from the M. abductor pollicis brevis could probably lead to a selective control of the thumb aperture as well allowing an independent control of the thumb.</p><p>Another advantage of the present approach is that it allows detecting the optimal stimulation pattern with relatively low current intensities. All previous methods necessitated supra-threshold stimulation, both in single pulse approaches <ref type="bibr" coords="7,377.81,316.72,16.11,8.98" target="#b21">[22]</ref> and in FES burst applications <ref type="bibr" coords="7,520.28,316.72,14.60,8.98" target="#b19">[20]</ref>, since they used kinematics as the primary read-out. The approach proposed in this study overcomes potential limitations of applying supra-threshold stimulation, i.e. the risk of fatigue and pain, and could constitute a solution for e.g. stroke patients with a disturbed kinematic response due to spasticity.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Potential Application for Neurorehabilitation</head><p>The selective activation of a single muscle, as shown in this study, or of a specific group of muscles, might be of particular relevance for neurorehabilitation where particularly the interplay between agonist and antagonist muscles might be disturbed. After stroke for example, the neuromuscular system might be impaired to a degree that the resulting spasticity would limit the detection of any muscle twitches. In this view, the proposed method might be particularly useful for those severely affected patients who show no residual hand movement at all. This technique might moreover allow a fine tuning of stimulation Fig. <ref type="figure" coords="7,78.07,274.72,4.42,7.36">5</ref> Measure of selectivity under the optimal stimulation pattern for finger extension, wrist extension and ulnar deviation, respectively. A high selectivity level was obtained for each of the identified sets of optimal patterns Fig. <ref type="figure" coords="7,78.07,709.38,4.42,7.36">6</ref> Finger extension (median value ± median absolute deviation) in the reference position (0°) and in the rotated forearm position (90°): No differences were found for each of the fingers. The amount of hand opening was thus comparable for the two forearm postures parameters as well, e.g. by disentangling the involvement of central pathways through spinal reflexes <ref type="bibr" coords="8,256.94,100.72,15.37,8.98" target="#b25">[26,</ref><ref type="bibr" coords="8,278.39,100.72,12.14,8.98" target="#b33">34,</ref><ref type="bibr" coords="8,56.69,112.74,13.13,8.98" target="#b34">35]</ref> and the peripheral contribution of the stimulation as detected with the M-wave, thus potentially limiting the stimulation induced muscle fatigue <ref type="bibr" coords="8,199.27,136.72,14.39,8.98" target="#b35">[36]</ref>.</p><p>Furthermore, the effectiveness of FES interventions may depend on the targeted function and the stimulation specificity. From a motor learning perspective, the ability to select a single hand function could potentially enhance motor recovery and facilitate CNS plasticity <ref type="bibr" coords="8,56.69,208.72,14.60,8.98" target="#b36">[37]</ref>. Especially, after neurological injuries such as stroke, and particularly in those therapeutic scenarios seeking to close the loop between motor intention/attempt and assistive technology, a targeted activation of the periphery might be essential <ref type="bibr" coords="8,135.33,256.74,15.09,8.98" target="#b37">[38,</ref><ref type="bibr" coords="8,154.07,256.74,11.32,8.98" target="#b38">39]</ref>. However, further research is necessary along these lines to better understand the physiological effects of FES on the CNS, and particularly on cortical connectivity <ref type="bibr" coords="8,157.04,292.74,14.60,8.98" target="#b39">[40]</ref>. With regard to the cortical physiology during peripheral input <ref type="bibr" coords="8,204.27,304.76,15.09,8.98" target="#b40">[41,</ref><ref type="bibr" coords="8,223.01,304.76,11.32,8.98" target="#b41">42]</ref>, recent findings in the field of brain-robot interfaces may inform FES applications as well <ref type="bibr" coords="8,162.14,328.74,14.60,8.98" target="#b42">[43]</ref>, with the goal of bridging the abilities and cortical networks of motor imagery and motor execution <ref type="bibr" coords="8,131.59,352.72,13.64,8.98" target="#b43">[44]</ref><ref type="bibr" coords="8,145.23,352.72,4.55,8.98" target="#b44">[45]</ref><ref type="bibr" coords="8,149.78,352.72,13.64,8.98" target="#b45">[46]</ref> and strengthening corticospinal connectivity <ref type="bibr" coords="8,127.12,364.74,14.60,8.98" target="#b46">[47]</ref>.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Possible limitations and future perspectives</head><p>Peak-to-peak values of M-waves were sufficient to detect optimal patterns, even though this approach did not always result in complete finger extensions. There was also no significant correlation between the M-wave peak-topeak value and the quality, i.e. functional characteristics, of the finger extension. A more careful inspection of the recorded signals may help to further differentiate between functional and non-functional extensions. This could possibly be achieved by extracting additional features from the recorded M-wave besides the peak-to-peak values used in this study, e.g. power and area of the M-wave.</p><p>Due to space limitations for positioning the EMG electrodes on the dorsal side of the forearm, we used monopolar instead of bipolar recordings. Although this approach might be less selective, noisier, and more affected by cross-talk during simultaneous recording and stimulation, it nevertheless resulted in the identification of a relevant number of selective patterns. However, we might have missed some stimulation patterns due crosstalk among EMG channels. Future studies need to explore whether bipolar EMG recordings might improve the scanning procedure and lead to a higher number of selective stimulation patterns.</p><p>In neurological patients with central nervous system damage, e.g. stroke, the peripheral pathways are intact <ref type="bibr" coords="8,56.69,700.75,15.86,8.98" target="#b30">[31]</ref> and M-waves can be elicited similarly to the findings in healthy subjects studied here. For practical application, however, EMG signal quality needs to be carefully considered for the effective application of the proposed technique, due to potential atrophy <ref type="bibr" coords="8,453.14,100.72,14.93,8.98" target="#b47">[48,</ref><ref type="bibr" coords="8,471.66,100.72,11.20,8.98" target="#b48">49]</ref>. In this context, electrode arrays allowing concurrent recording and stimulation with a higher spatial specificity, i.e. lower inter-electrode distance, could potentially improve the selectivity of the proposed approach. Future studies could simultaneously record and stimulate both sides of the forearm, integrating the selective activation of flexor muscles in a bid to develop a system that is able to elicit selective hand opening and grasping together with the functional stabilization of the wrist.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Conclusions</head><p>This study has introduced a novel method to automatically select stimulation electrodes for FES. The protocol identifies a small subset of electrodes from an array entirely based on the electrophysiological response to stimulation. This technique is capable of eliciting a full hand opening. The complete procedure is fast, comfortable and precise with regard to the targeted muscles. This could be particularly important for rehabilitation protocols addressing muscle synergies and activities of daily living.</p></div><figure xmlns="http://www.tei-c.org/ns/1.0" xml:id="fig_0" coords="5,62.70,289.74,469.58,7.36;5,62.70,300.01,329.14,7.06"><head>Fig. 2 M</head><label>2</label><figDesc>Fig.2M-waves during the scanning procedure for the identified optimal stimulation patterns for one representative subject (black: EDC, red: ECR, blue: ECU). Thick lines represent the average M-wave for the corresponding set of stimulation patterns</figDesc></figure>
+<figure xmlns="http://www.tei-c.org/ns/1.0" xml:id="fig_1" coords="6,62.70,246.09,467.97,7.36;6,62.70,256.42,458.01,7.06"><head>Fig. 3 Fig. 4</head><label>34</label><figDesc>Fig. 3 Finger extension movements (median value ± median absolute deviation) comparing different stimulation patterns (FEP = Finger Extension Pattern, WEP = Wrist Extension Pattern, UDP = Ulnar Deviation Pattern). In this case WEP and UDP constitute the control measurements for FEP</figDesc></figure>
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+		<back>
+
+			<div type="acknowledgement">
+<div xmlns="http://www.tei-c.org/ns/1.0" />
+			</div>
+
+			<div type="annex">
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Competing interests</head><p>The authors declare that they have no competing interests. </p></div>			</div>
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+				<title level="a" type="main">Clinical Characteristics, Electrophysiology, and Skin Biopsy of 38 Peripheral Neuropathy Cases with Small Fiber Involvement of Various Etiologies</title>
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+				<publisher>Medknow</publisher>
+				<availability status="unknown"><p>Copyright Medknow</p>
+				</availability>
+				<date type="published" when="2017">2017</date>
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+						<author>
+							<persName coords="1,386.55,179.95,53.02,7.91"><roleName>Dr</roleName><forename type="first">Xu-Sheng</forename><surname>Huang</surname></persName>
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+							<persName coords="1,155.71,179.95,20.79,7.91"><forename type="first">Bo</forename><surname>Sun</surname></persName>
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+							<persName coords="1,182.03,179.95,28.94,7.91"><forename type="first">Li-Zhi</forename><surname>Liu</surname></persName>
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+							<affiliation key="aff0">
+								<orgName type="department">Department of Neurology</orgName>
+								<orgName type="institution">Chinese People&apos;s Liberation Army General Hospital</orgName>
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+									<postCode>100853</postCode>
+									<settlement>Beijing</settlement>
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+								<orgName type="department">Department of Neurology, Chinese People&apos;s Liberation Army General Hospital</orgName>
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+									<addrLine>No. 28 Fuxing Road</addrLine>
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+									<settlement>Beijing</settlement>
+									<country key="CN">China</country>
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+						<title level="a" type="main">Clinical Characteristics, Electrophysiology, and Skin Biopsy of 38 Peripheral Neuropathy Cases with Small Fiber Involvement of Various Etiologies</title>
+					</analytic>
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+						<title level="j" type="main">Chinese Medical Journal</title>
+						<title level="j" type="abbrev">Chin Med J</title>
+						<idno type="ISSN">0366-6999</idno>
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+							<publisher>Medknow</publisher>
+							<biblScope unit="volume">130</biblScope>
+							<biblScope unit="issue">14</biblScope>
+							<biblScope unit="page">1683</biblScope>
+							<date type="published" when="2017" />
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+					<idno type="DOI">10.4103/0366-6999.209897</idno>
+					<idno type="PMID">28685718</idno>
+					<idno type="PMCID">PMC5520555</idno>
+					<note type="submission">Received: 28-02-2017</note>
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+					<term>Impaired Glucose Tolerance</term>
+					<term>Intraepidermal Nerve Fiber Density</term>
+					<term>Metabolic Syndrome</term>
+					<term>Nerve Conduction Studies</term>
+					<term>Small Fiber Neuropathy</term>
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+<div xmlns="http://www.tei-c.org/ns/1.0"><p>Peripheral neuropathy (PN) can be categorized according to the nerves involved. Pure small fiber neuropathy (SFN) affects only the small fiber nerves, pure large fiber neuropathy affects only large fiber nerves, and mixed small and large fiber neuropathy affects both types of nerves. <ref type="bibr" coords="1,215.69,563.00,6.67,5.65" target="#b0">[1]</ref> Small diameter, thinly myelinated Aδ and unmyelinated C fibers are primarily affected by SFN, resulting in sensory and/or autonomic symptoms such as allodynia, hyperalgesia, palpitations, and hyper/hypohidrosis. <ref type="bibr" coords="1,153.45,611.00,6.80,5.65" target="#b0">[1,</ref>2]  Traditional nerve conduction studies (NCS) measure the integrity and function of large fibers independent of small fiber function. Thus, the evaluation of small fiber function has been challenging until the introduction of skin biopsies assessing intraepidermal nerve fiber density (IENFD). <ref type="bibr" coords="1,344.15,520.46,6.63,5.65" target="#b1">[3]</ref> Recently, other techniques such as quantitative sensory testing, contact heat evoked potentials, as well as questionnaires and scales (e.g., the SFN and Symptom</p></div>
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+<div xmlns="http://www.tei-c.org/ns/1.0"><p>Inventory Questionnaire [SIQ]), have been applied to evaluate small fiber damage in patients. <ref type="bibr" coords="2,169.29,73.28,6.64,5.65" target="#b0">[1,</ref><ref type="bibr" coords="2,175.93,73.28,2.21,5.65" target="#b2">[4]</ref><ref type="bibr" coords="2,178.15,73.28,2.21,5.65" target="#b3">[5]</ref><ref type="bibr" coords="2,180.36,73.28,4.43,5.65" target="#b4">[6]</ref> However, skin biopsies are still considered the single most reliable and necessary test in the diagnosis of SFN. <ref type="bibr" coords="2,137.41,97.90,6.49,5.65" target="#b0">[1,</ref><ref type="bibr" coords="2,143.90,97.90,4.33,5.65" target="#b5">7]</ref> Various etiologies have been shown to be associated with SFN. <ref type="bibr" coords="2,74.73,128.19,6.70,5.65" target="#b6">[8,</ref><ref type="bibr" coords="2,81.43,128.19,4.47,5.65" target="#b7">9]</ref> Therefore, obtaining a thorough medical history to determine possible causative factors should be carried out in patients suspected having SFN. In general, impaired glucose tolerance (IGT) is the most prevalent etiology resulting in SFN. <ref type="bibr" coords="2,74.33,177.43,6.68,5.65" target="#b6">[8]</ref> In addition to IGT, diabetes, dyslipidemia, connective tissue diseases, HIV infection, hepatitis C and other hepatic disorders, nutritional diseases, hyper/hypothyroidism, paraneoplastic syndromes, and neurotoxic agents such as oxaliplatin, paclitaxel, and vincristine <ref type="bibr" coords="2,213.47,226.67,7.23,5.65" target="#b0">[1,</ref><ref type="bibr" coords="2,220.70,226.67,7.23,5.65" target="#b8">10]</ref> have all been shown to result in SFN. Thorough screening in patients could help identifying the etiology; however, no definitive etiologies are easily identifiable in some patients, which are regarded as having idiopathic neuropathy. <ref type="bibr" coords="2,218.83,275.91,6.80,5.65" target="#b6">[8]</ref> The occurrence of SFN can precede the occurrence of the primary disease, and a routine follow-up should be performed to identify the etiology. <ref type="bibr" coords="2,88.51,312.84,7.40,5.65" target="#b9">[11]</ref><ref type="bibr" coords="2,95.91,312.84,2.47,5.65" target="#b10">[12]</ref><ref type="bibr" coords="2,95.91,312.84,2.47,5.65" target="#b11">[13]</ref><ref type="bibr" coords="2,98.38,312.84,7.40,5.65" target="#b12">[14]</ref> This study aimed to investigate the clinical features, electrophysiological parameters, and IENFD in patients with small fiber PN resulting from a variety of etiologies, and to compare the severity of disease among idiopathic PN, IGT-related PN, and MS-related PN.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Methods</head></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Ethical approval</head><p>The study was approved by the Ethics Committee of the Chinese People's Liberation Army (PLA) General Hospital, and all patients gave written informed consent before the study.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Patients</head><p>Sixty-eight consecutive patients with small fiber PN presenting to the department of neurology in the Chinese PLA General Hospital from December 20, 2013, to May 31, 2016, were enrolled. All patients presented with sensory or autonomic symptoms, which included disturbance of pinprick sensation and temperature sensation, allodynia, hyperalgesia, numbness, coldness, tightness, burning sensation, tingling sensation, bedsheet intolerance, restless legs, sensation of walking on sand or pebbles, muscle cramps, palpitations, hot flushes, hyper/hypohidrosis, skin discoloration, difficulty in urination, constipation/ diarrhea, sexual dysfunction, or sicca syndrome. A detailed medical history was taken from all patients, and a variety of laboratory tests were performed to screen for underlying etiologies. We identified 38 suitable patients with idiopathic PN (n = 17), IGT-related PN (n = 12), and metabolic syndrome (MS)-related PN (n = 9). Patients were divided into three groups according to the above etiologies. Patients included in the study were 22 males and 16 females, with a mean age of 54 ± 14 years.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Etiology screening</head><p>A detailed medical history was taken, including a history of smoking and alcohol consumption, renal disorders, nutritional diseases, hyper/hypothyroidism, hematological disorders, malignancy, connective tissue diseases, infection with hepatitis C and other hepatic disorders, paraneoplastic syndromes, a medication history, a history of neurotoxic agents, and the presence of a family history of any neuropathy.</p><p>Laboratory tests to determine disease etiology included a complete blood count, erythrocyte sedimentation rate, C-reactive protein, renal and hepatic function, lipid profile, fasting glucose, glycosylated hemoglobin, oral glucose tolerance test, folate and Vitamin B 12 , thyroid function test, antinuclear antibody, anti-extractable nuclear antigens antibody, serum protein electrophoresis, and tumor markers. Diagnosis of impaired fasting glucose, IGT, and diabetes was based on comparison with the diagnostic criteria published by the World Health Organization. <ref type="bibr" coords="2,444.60,287.16,9.71,5.65" target="#b13">[15]</ref> </p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Clinical characteristics</head><p>Sensory and autonomic symptoms were recorded using the 13-item SFN-SIQ, which assessed factors including overly-sensitive leg skin, burning feet, bedsheet intolerance, restless legs at night, changes in sweating patterns, presence of diarrhea or constipation, urinary tract problems (including hesitation and incontinence), dry eyes, dry mouth, dizziness when standing up, palpitations, and hot flushes. A 4-point Likert scale (0: never present; 1: sometimes; 2: often; and 3: always present) was used to determine symptom severity. <ref type="bibr" coords="2,530.66,415.75,6.44,5.65" target="#b3">[5,</ref><ref type="bibr" coords="2,537.10,415.75,4.29,5.65" target="#b4">6]</ref> A neurological examination was also performed by an experienced neurologist and included examinations for muscle strength, pinprick and vibration sensation, and deep tendon reflexes. Pinprick and vibration perception were conducted using a disposable safety needle and 128 Hz tuning fork, respectively. As previously described, the neurologist was blinded to the clinical symptoms. <ref type="bibr" coords="2,505.22,506.62,9.71,5.65" target="#b14">[16]</ref> Nerve conduction studies Skin temperature was maintained at or above 32°C during the examination. NCS were performed using surface electrodes on the tibial, peroneal, and sural nerves in both lower limbs using the Keypoint electromyography (EMG) system (Medoc Ltd., Ramat Yishai, Israel). Motor conduction velocities (MCVs), proximal and distal compound muscle action potentials (CMAPs) of the tibial and peroneal nerves, as well as sensory conduction velocities and sensory nerve action potential of the sural nerve were measured. Results were compared with reference values utilized by the EMG laboratory of Chinese PLA General Hospital.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Assessment of intraepidermal nerve fiber density</head><p>Skin biopsies were conducted using a 3-mm diameter skin biopsy punch (Acuderm Inc., Fort Lauderdale, USA) approximately 10 cm above the lateral malleolus under local anesthesia. Specimens were acquired from the side with the most severe symptoms. The right side was selected if the clinical presentation was identical on both sides. Specimens were immunostained using rabbit polyclonal antibodies to human protein gene product 9.5 (Chemicon International Inc., Temecula, USA) as previously described. <ref type="bibr" coords="3,244.85,97.51,7.19,5.65" target="#b1">[3,</ref><ref type="bibr" coords="3,252.04,97.51,7.19,5.65" target="#b15">17]</ref> IENFs were counted in at least three sections as previously described, by two independent researchers blind to the clinical data. <ref type="bibr" coords="3,106.85,134.05,9.89,5.65" target="#b16">[18]</ref> A computerized imaging system -Image Pro Plus 6.0 (Media Cybernetics, Silver Spring, USA) was used to measure epidermal length. Thus, the average IENFD (number of fibers/mm) was calculated. An IENFD below the fifth percentile of the worldwide normative reference value was considered abnormal. <ref type="bibr" coords="3,225.73,194.95,9.89,5.65" target="#b17">[19]</ref> Statistical analysis SPSS version 19.0 (IBM, Armonk, USA) was applied to perform statistical analysis. Partial correlations, which were controlled for age and gender, were analyzed to determine relationships among the IENFD and visual analog scale (VAS), SFN-SIQ, and nerve conduction parameters. Analysis of variance (ANOVA) was conducted to compare the differences among idiopathic PN, IGT-related PN, and MS-related PN groups. Post hoc analyses (Least Significant Difference and Student-Newman-Keuls) were performed if significant differences were identified in the ANOVA. Kruskal-Wallis test was conducted if data were not normally distributed.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>results</head></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Clinical characteristics</head><p>All patients presented with a disturbance in pinprick sensation, temperature sensation, and/or allodynia or hyperalgesia, which was in accordance with the clinical manifestations of SFN.</p><p>Mean VAS score of all patients included was 4.66 ± 3.56. Mean VAS scores of all groups are presented in Table <ref type="table" coords="3,281.01,471.82,3.75,9.69" target="#tab_0">1</ref>. No significant differences in VAS scores were revealed among the three groups ( χ 2 = 2.102, P = 0.350). Mean SFN-SIQ score of all patients included was 5.74 ± 2.42. Mean SFN-SIQ scores for all groups were also presented in Table <ref type="table" coords="3,77.18,532.72,3.71,9.69" target="#tab_0">1</ref>. The mean SFN-SIQ score was significantly different among the three groups (F = 14.433, P &lt; 0.001). The mean SFN-SIQ score was significantly increased (P &lt; 0.001) in the MS-related PN group compared to IGT-related PN group. The mean SFN-SIQ score was significantly increased (P &lt; 0.001) in MS-related PN group compared to the idiopathic PN group (P &lt; 0.001).</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Nerve conduction study and skin biopsy analysis</head><p>NCS was abnormal in 17 patients and normal in 21 patients. According to the international normative reference value, IENFD parameters were abnormal in 15 patients and normal in 23 patients. Eight patients were diagnosed with pure SFN, seven patients were diagnosed with mixed small and large fiber neuropathy, 10 patients were diagnosed with pure large fiber neuropathy, and 13 patients did not display any abnormalities in NCS and skin biopsies.</p><p>In the IGT-related PN group, three patients were diagnosed with mixed small and large fiber neuropathy, six patients were diagnosed with pure large fiber neuropathy, and three patients did not display any abnormalities in NCS and skin biopsies.</p><p>In the MS-related PN group, three patients were diagnosed with pure SFN, four patients were diagnosed with mixed small and large fiber neuropathy, one patient was diagnosed with pure large fiber neuropathy, and one patient did not display any abnormalities in NCS and skin biopsies.</p><p>In the idiopathic PN group, five patients were diagnosed with pure SFN, three patients were diagnosed with pure large fiber neuropathy, and nine patients did not display any abnormalities in NCS and skin biopsies.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Relationships among intraepidermal nerve fiber density, visual analog scale, small fiber neuropathy and symptom inventory questionnaire, and nerve conduction studies</head><p>The partial correlation coefficient between the IENFD and SFN-SIQ was r = −0.668 (P &lt; 0.001), indicating a moderate correlation. The partial correlation coefficient between the IENFD and MCV of the tibial nerve was r = 0.372 (P = 0.025), indicating low correlation. The partial correlation coefficient between the IENFD and proximal and distal CMAP of the tibial nerve was r = 0.383 (P = 0.021) and r = 0.358 (P = 0.032), respectively, indicating a low correlation. The partial correlation coefficient between the IENFD and MCV of the peroneal nerve was r = 0.399 (P = 0.016), indicating a low correlation.</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Comparison of parameters among different groups</head><p>IENFD parameters were significantly different among all three groups ( χ 2 = 9.901, P = 0.007). IENFD was significantly decreased ( χ 2 = 23.000, P = 0.003) in the MS-related PN group compared to the idiopathic PN group. IENFD was also decreased ( χ 2 = 27.000, P = 0.058) in the MS-related PN group compared to the IGT-related PN group, however, with no significant difference. IENFD was also decreased ( χ 2 = 64.000, P = 0.097) in the IGT-related PN group compared to the idiopathic PN group, again with no significant difference [Table <ref type="table" coords="3,421.54,674.26,3.61,9.69" target="#tab_1">2</ref>].</p><p>The MCV of the tibial nerve was significantly different among all three groups ( χ 2 = 8.172, P = 0.017). The MCV of the tibial nerve was also significantly decreased ( χ 2 = 29.503, P &lt; 0.009) in the MS-related PN group compared to the idiopathic PN group. The proximal and distal CMAP of the tibial nerve was significantly different between all three groups (F = 4.336, P = 0.021; F = 3.262, P = 0.049), respectively. The proximal CMAP of the tibial nerve was significantly decreased (P = 0.017) in the IGT-related PN group compared to the idiopathic PN group. The proximal CMAP of the tibial nerve was significantly decreased (P = 0.022) in the MS-related PN group compared to the idiopathic PN group. Furthermore, the distal CMAP of the tibial nerve was significantly decreased (P = 0.035) in the IGT-related PN group compared to the idiopathic PN group. The distal CMAP of the tibial nerve was significantly decreased (P = 0.049) in the MS-related PN group compared to the idiopathic PN group [Table <ref type="table" coords="4,107.62,453.85,3.61,9.69" target="#tab_1">2</ref>].</p></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>dIscussIon</head><p>This study identified that IENFD weakly correlated with the MCV and proximal and distal CMAPs of the tibial nerve. IENFD also weakly correlated with the MCV of the peroneal nerve. However, a conflicting study has previously demonstrated that IENFD does not relate to NCS parameters in patients with IGT and early diabetes, <ref type="bibr" coords="4,216.98,557.39,9.80,5.65" target="#b18">[20]</ref> indicating that the severity of small and large fiber damage may not coincide. Such findings may be accounted for by the fact that patients in the study were in the initial stages of IGT or diabetes-associated neuropathy, suggesting they may not have extensive nerve damage. The initial stages of IGT or diabetes-associated neuropathy involve small fiber damage, eventually progressing to large fiber involvement. <ref type="bibr" coords="4,259.92,642.53,9.80,5.65" target="#b18">[20]</ref> The aforementioned report enrolled only 14 patients, six with IGT and eight with early-stage diabetes. The small number of patients in that study may have led to a certain degree of outcome bias. In another previously published study, <ref type="bibr" coords="4,271.20,691.18,9.80,5.65" target="#b19">[21]</ref> a low to moderate correlation was identified between IENFD and the electrophysiological parameters in diabetic patients, which was in accordance with the findings in our study.</p><p>One study <ref type="bibr" coords="4,97.95,739.83,10.10,5.65" target="#b19">[21]</ref> has indicated IENFD correlated with the various parameters of NCS, which is in accordance with our study. Furthermore, another study <ref type="bibr" coords="4,443.31,307.37,9.80,5.65" target="#b20">[22]</ref> found NCS remained within the normal range in type 2 diabetes, but there was a small significant decline after 5 years; IENFD changed from normal to abnormal after 5-year follow-up. This suggests that during early stages of IGT/diabetes-associated neuropathy, only IENFD are involved, and IENFD does not correlate initially with NCS parameters. Nevertheless, as the disease progresses, IENFD and electrophysiological parameters correlate, suggesting advancement in the degree of nerve damage. A recent study indicated that IENFD was able to serve as a marker of the course of diabetic neuropathy. <ref type="bibr" coords="4,354.02,441.50,9.80,5.65" target="#b21">[23]</ref> This study also demonstrated that IENFD significantly decreased in the MS-related group compared to the idiopathic PN group. The mean IENFD in the MS-related group was lower than that of the IGT-related group, although was not significantly different. The mean IENFD in the IGT-related group was lower than that of the idiopathic PN group, again with no significant difference. The MCV of the tibial nerve was significantly decreased in the MS-related PN group compared to the idiopathic PN group, and the proximal and distal CMAP of the tibial nerve was significantly different between all three groups. In a study focusing on IGT and diabetes-associated neuropathy, IENFD and electrophysiological parameters were reduced in diabetics compared to IGT-related PN patients, although not significantly. <ref type="bibr" coords="4,453.47,630.08,10.06,5.65" target="#b18">[20]</ref> The small number of included patients in that study may account for such findings. In our study, the number of included patients was comparably larger in comparison to other previously published reports, with significant differences in IENFD and NCS parameters between IGT/MS-related and idiopathic PN groups. This suggests that improving the number of patients in such a study might increase the power of the statistical findings associated with clinical parameters. The IENFD and various NCS parameters in the MS-related PN group were decreased compared to the IGT-related PN group and idiopathic PN groups. Another study also indicated distal leg IENFD was significantly reduced in both MS and diabetic groups. <ref type="bibr" coords="5,118.28,108.97,9.71,5.65" target="#b22">[24]</ref> This may be caused by several factors. First, obesity, hypertension, and in particular, hyperlipidemia are crucial risk factors for small fiber damage. <ref type="bibr" coords="5,233.01,132.97,9.67,5.65" target="#b19">[21,</ref><ref type="bibr" coords="5,242.68,132.97,7.25,5.65" target="#b23">25,</ref><ref type="bibr" coords="5,249.93,132.97,7.25,5.65" target="#b24">26]</ref> Indeed, MS is more prevalent in patients with severe PN than with mild/moderate PN, <ref type="bibr" coords="5,131.25,156.97,9.71,5.65" target="#b23">[25]</ref> supporting our finding that IENFD in the MS-related PN group was lower. Second, in the MS-related PN group, six patients were diagnosed with diabetes mellitus, and three were diagnosed with IGT, which may partially explain the lower IENFD in the MS-related PN group compared with the IGT-related PN groups. In a recent study, dynamic worsening was present in relation to changes in glucose tolerance status, which was in accordance with our findings. <ref type="bibr" coords="5,124.73,252.97,9.71,5.65" target="#b25">[27]</ref> What's more, the IENFD and electrophysiological parameters in the idiopathic PN group were the least affected. The occurrence of SFN can precede the presentation of primary disease, with approximately 35-50% of idiopathic SFN cases being confirmed as IGT-related SFN. <ref type="bibr" coords="5,195.65,318.64,7.18,5.65" target="#b9">[11]</ref><ref type="bibr" coords="5,202.83,318.64,2.39,5.65" target="#b10">[12]</ref><ref type="bibr" coords="5,202.83,318.64,2.39,5.65" target="#b11">[13]</ref><ref type="bibr" coords="5,205.23,318.64,7.18,5.65" target="#b12">[14]</ref><ref type="bibr" coords="5,212.40,318.64,7.18,5.65" target="#b26">28]</ref> Dyslipidemia has also been identified in patients with idiopathic PN in routine follow-up. <ref type="bibr" coords="5,95.83,342.64,9.71,5.65" target="#b24">[26]</ref> Patients presenting with idiopathic PN in our study may precede IGT or dyslipidemia, thus, small and large fiber damage in the idiopathic PN group was less severe than in the IGT-and MS-related PN groups. Therefore, a routine follow-up to search for etiologies associated with idiopathic PN is of vital importance.</p><p>It is important, to mention that a limitation of this study was that the relatively small number of the patients. Large sample studies are warranted, and the relationship between dyslipidemia and PN should be evaluated in further studies.</p><p>In conclusion, the IENFD of patients included in our study weakly correlated with certain electrophysiological parameters. Peripheral nerves, including both small and large fibers, were more severely involved in MS-related PN than in idiopathic PN. All possible auxiliary examinations and routine follow-up should be conducted to search for underlying etiologies in idiopathic PN patients.</p></div><figure xmlns="http://www.tei-c.org/ns/1.0" type="table" xml:id="tab_0" coords="3,56.60,636.15,229.09,112.95"><head>Table 1 : VAS and SFN-SIQ scores of different types of peripheral neuropathy patients with small fiber involvement</head><label>1</label><figDesc></figDesc><table coords="3,56.60,665.12,229.09,83.99"><row><cell>Items</cell><cell>IGT-related</cell><cell>MS-related</cell><cell>Idiopathic</cell></row><row><cell></cell><cell>PN (n = 12)</cell><cell>PN (n = 9)</cell><cell>PN (n = 17)</cell></row><row><cell>VAS</cell><cell>3.75 ± 3.91</cell><cell>6.00 ± 3.91</cell><cell>4.59 ± 3.08</cell></row><row><cell>SFN-SIQ</cell><cell>5.33 ± 1.67</cell><cell>8.56 ± 2.51</cell><cell>4.53 ± 1.55</cell></row><row><cell cols="4">Data are shown as mean ± SD. VAS: Visual analog scale; SFN-SIQ: The</cell></row><row><cell cols="4">Small Fiber Neuropathy and Symptom Inventory Questionnaire; IGT:</cell></row><row><cell cols="4">Impaired glucose tolerance; MS: Metabolic syndrome; PN: Peripheral</cell></row><row><cell cols="2">neuropathy; SD: Standard deviation.</cell><cell></cell><cell></cell></row></table></figure>
+<figure xmlns="http://www.tei-c.org/ns/1.0" type="table" xml:id="tab_1" coords="4,56.72,66.96,482.03,212.93"><head>Table 2 : IENFD and NCS parameters among different types of peripheral neuropathy patients with small fiber involvement</head><label>2</label><figDesc>Data are shown as n or mean ± SD. *F value. IENFD: Intraepidermal nerve fiber density; TIBI.MCV: Motor conduction velocity of tibial nerve; T.PCMAP: Proximal compound muscle action potential of tibial nerve; T.DCMAP: Distal compound muscle action potential of tibial nerve; PERI.MCV: Motor conduction velocity of peroneal nerve; P.PCMAP: Proximal compound muscle action potential of peroneal nerve; P.DCMAP (mV): Distal compound muscle action potential of peroneal nerve; SURA.SCV: Sensory conduction velocity of sural nerve; S.SNAP: Sensory nerve action potential of sural nerve; IGT: Impaired glucose tolerance; MS: Metabolic syndrome; PN: Peripheral neuropathy; SD: Standard deviation; NCS: Nerve conduction studies.</figDesc><table coords="4,56.72,83.75,481.16,146.68"><row><cell>Parameters</cell><cell>IGT-related PN (n = 12)</cell><cell>MS-related PN (n = 9)</cell><cell>Idiopathic PN (n = 17)</cell><cell>F or χ 2</cell><cell>P</cell></row><row><cell>Age (years)</cell><cell>56.8 ± 12.7</cell><cell>60.2 ± 15.6</cell><cell>49.6 ± 14.2</cell><cell>1.924*</cell><cell>0.161</cell></row><row><cell>Gender (male), n</cell><cell>7</cell><cell>5</cell><cell>10</cell><cell>0.424</cell><cell>0.809</cell></row><row><cell>IENFD (number of</cell><cell>4.66 ± 5.25</cell><cell>2.57 ± 2.60</cell><cell>6.25 ± 2.51</cell><cell>9.901</cell><cell>0.007</cell></row><row><cell>fibers/mm)</cell><cell></cell><cell></cell><cell></cell><cell></cell><cell></cell></row><row><cell>TIBI.MCV (m/s)</cell><cell>40.48 ± 13.35</cell><cell>36.34 ± 15.68</cell><cell>47.44 ± 6.09</cell><cell>8.172</cell><cell>0.017</cell></row><row><cell>T.PCMAP (mV)</cell><cell>5.26 ± 3.39</cell><cell>5.03 ± 5.24</cell><cell>10.31 ± 6.42</cell><cell>4.336*</cell><cell>0.021</cell></row><row><cell>T.DCMAP (mV)</cell><cell>7.41 ± 4.91</cell><cell>7.32 ± 7.09</cell><cell>12.72 ± 6.97</cell><cell>3.262*</cell><cell>0.049</cell></row><row><cell>PERI.MCV (m/s)</cell><cell>42.24 ± 13.78</cell><cell>35.47 ± 20.24</cell><cell>47.98 ± 5.24</cell><cell>2.926</cell><cell>0.232</cell></row><row><cell>P.PCMAP (mV)</cell><cell>3.54 ± 2.64</cell><cell>4.10 ± 3.73</cell><cell>5.79 ± 2.84</cell><cell>2.189*</cell><cell>0.127</cell></row><row><cell>P.DCMAP (mV)</cell><cell>4.15 ± 3.20</cell><cell>4.86 ± 3.92</cell><cell>6.81 ± 2.27</cell><cell>3.024*</cell><cell>0.061</cell></row><row><cell>SURA.SCV (m/s)</cell><cell>34.43 ± 25.60</cell><cell>35.53 ± 27.02</cell><cell>46.48 ± 18.17</cell><cell>1.792</cell><cell>0.408</cell></row><row><cell>S.SNAP (µV)</cell><cell>4.68 ± 5.25</cell><cell>7.33 ± 8.30</cell><cell>9.59 ± 9.40</cell><cell>1.314*</cell><cell>0.282</cell></row></table></figure>
+		</body>
+		<back>
+			<div type="annex">
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Financial support and sponsorship</head></div>
+<div xmlns="http://www.tei-c.org/ns/1.0"><head>Conflicts of interest</head><p>There are no conflicts of interest.</p></div>			</div>
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+		<author>
+			<persName><forename type="first">Tomonori</forename><surname>Nakamura</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Yumiko</forename><surname>Arimura</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Kimiyoshi</forename><surname>Arimura</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Soroku</forename><surname>Yagihashi</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Yoshihiko</forename><surname>Nishio</surname></persName>
+		</author>
+		<author>
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+		</author>
+		<idno type="DOI">10.1016/j.diabres.2012.09.026</idno>
+		<idno type="PMID">23006635</idno>
+	</analytic>
+	<monogr>
+		<title level="j">Diabetes Research and Clinical Practice</title>
+		<title level="j" type="abbrev">Diabetes Research and Clinical Practice</title>
+		<idno type="ISSN">0168-8227</idno>
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+			<biblScope unit="volume">99</biblScope>
+			<biblScope unit="issue">1</biblScope>
+			<biblScope unit="page" from="24" to="29" />
+			<date type="published" when="2013-01" />
+			<publisher>Elsevier BV</publisher>
+		</imprint>
+	</monogr>
+</biblStruct>
+
+<biblStruct coords="5,320.81,741.35,220.58,7.75;6,68.06,61.48,220.58,7.75;6,68.06,71.08,220.58,7.75;6,68.06,80.68,60.67,7.75" xml:id="b20">
+	<analytic>
+		<title level="a" type="main">Small and large fiber neuropathy in those with type 1 and type 2 diabetes: a 5-year follow-up study</title>
+		<author>
+			<persName><forename type="first">Sissel</forename><surname>Løseth</surname></persName>
+		</author>
+		<author>
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+		</author>
+		<author>
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+		</author>
+		<author>
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+		</author>
+		<author>
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+		</author>
+		<idno type="DOI">10.1111/jns.12154</idno>
+		<idno type="PMID">26663481</idno>
+	</analytic>
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+			<biblScope unit="volume">21</biblScope>
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+			<biblScope unit="page" from="15" to="21" />
+			<date type="published" when="2016-03" />
+			<publisher>Wiley</publisher>
+		</imprint>
+	</monogr>
+</biblStruct>
+
+<biblStruct coords="6,68.06,90.28,220.58,7.75;6,68.06,99.88,220.58,7.75;6,68.06,109.48,220.58,7.75;6,68.06,119.08,130.66,7.75" xml:id="b21">
+	<analytic>
+		<title level="a" type="main">Intraepidermal nerve-fibre density as a biomarker of the course of neuropathy in patients with Type 2 diabetes mellitus</title>
+		<author>
+			<persName coords=""><forename type="first">S</forename><surname>Divisova</surname></persName>
+		</author>
+		<author>
+			<persName coords=""><forename type="first">E</forename><surname>Vlckova</surname></persName>
+		</author>
+		<author>
+			<persName coords=""><forename type="first">I</forename><surname>Srotova</surname></persName>
+		</author>
+		<author>
+			<persName coords=""><forename type="first">S</forename><surname>Kincova</surname></persName>
+		</author>
+		<author>
+			<persName coords=""><forename type="first">M</forename><surname>Skorna</surname></persName>
+		</author>
+		<author>
+			<persName coords=""><forename type="first">L</forename><surname>Dusek</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">P</forename><surname>Dubovy</surname></persName>
+		</author>
+		<author>
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+		</author>
+		<idno type="DOI">10.1111/dme.12890</idno>
+		<idno type="PMID">26344697</idno>
+	</analytic>
+	<monogr>
+		<title level="j">Diabetic Medicine</title>
+		<title level="j" type="abbrev">Diabet. Med.</title>
+		<idno type="ISSN">0742-3071</idno>
+		<imprint>
+			<biblScope unit="volume">33</biblScope>
+			<biblScope unit="issue">5</biblScope>
+			<biblScope unit="page" from="650" to="654" />
+			<date type="published" when="2015-09-07" />
+			<publisher>Wiley</publisher>
+		</imprint>
+	</monogr>
+</biblStruct>
+
+<biblStruct coords="6,68.06,128.68,220.57,7.75;6,68.06,138.28,220.58,7.75;6,68.06,147.88,220.58,7.75;6,68.06,157.48,63.33,7.75" xml:id="b22">
+	<analytic>
+		<title level="a" type="main">Supervised exercise improves cutaneous reinnervation capacity in metabolic syndrome patients</title>
+		<author>
+			<persName coords=""><forename type="first">J</forename><forename type="middle">Robinson</forename><surname>Singleton</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Robin</forename><forename type="middle">L</forename><surname>Marcus</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Margaret</forename><forename type="middle">K</forename><surname>Lessard</surname></persName>
+		</author>
+		<author>
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+		</author>
+		<author>
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+		</author>
+		<idno type="DOI">10.1002/ana.24310</idno>
+		<idno type="PMID">25388934</idno>
+		<idno type="PMCID">PMC4293306</idno>
+		<idno type="ark">ark:/67375/WNG-GDGDR6SZ-W</idno>
+		<idno type="istexId">CE46A9EE2399DD5CC848CC566BF5E375DA47B3EE</idno>
+		<ptr type="open-access" target="http://europepmc.org/articles/pmc4293306?pdf=render" />
+	</analytic>
+	<monogr>
+		<title level="j">Annals of Neurology</title>
+		<title level="j" type="abbrev">Ann Neurol.</title>
+		<idno type="ISSN">0364-5134</idno>
+		<imprint>
+			<biblScope unit="volume">77</biblScope>
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+			<biblScope unit="page" from="146" to="153" />
+			<date type="published" when="2014-12-04" />
+			<publisher>Wiley</publisher>
+		</imprint>
+	</monogr>
+</biblStruct>
+
+<biblStruct coords="6,68.06,167.08,220.58,7.75;6,320.81,61.48,220.58,7.75;6,320.81,71.08,126.22,7.75" xml:id="b23">
+	<analytic>
+		<title level="a" type="main">Metabolic Syndrome in Small Fiber Sensory Neuropathy</title>
+		<author>
+			<persName><forename type="first">Lan</forename><surname>Zhou</surname></persName>
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+		<author>
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+	</analytic>
+	<monogr>
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+		<title level="j" type="abbrev">Journal of Clinical Neuromuscular Disease</title>
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+			<publisher>Ovid Technologies (Wolters Kluwer Health)</publisher>
+		</imprint>
+	</monogr>
+</biblStruct>
+
+<biblStruct coords="6,320.81,80.68,220.58,7.75;6,320.81,90.28,220.58,7.75;6,320.81,99.88,85.78,7.75" xml:id="b24">
+	<analytic>
+		<title level="a" type="main">Idiopathic neuropathy patients are at high risk for metabolic syndrome</title>
+		<author>
+			<persName coords=""><forename type="first">A</forename><forename type="middle">Gordon</forename><surname>Smith</surname></persName>
+		</author>
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+		<author>
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+		</author>
+		<idno type="DOI">10.1016/j.jns.2008.06.005</idno>
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+		<idno type="PMCID">PMC2576295</idno>
+		<ptr type="open-access" target="http://europepmc.org/articles/pmc2576295?pdf=render" />
+	</analytic>
+	<monogr>
+		<title level="j">Journal of the Neurological Sciences</title>
+		<title level="j" type="abbrev">Journal of the Neurological Sciences</title>
+		<idno type="ISSN">0022-510X</idno>
+		<imprint>
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+			<biblScope unit="page" from="25" to="28" />
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+			<publisher>Elsevier BV</publisher>
+		</imprint>
+	</monogr>
+</biblStruct>
+
+<biblStruct coords="6,320.81,109.48,220.57,7.75;6,320.81,119.08,220.58,7.75;6,320.81,128.68,220.58,7.75;6,320.81,138.28,211.31,7.75" xml:id="b25">
+	<analytic>
+		<title level="a" type="main">Corneal Confocal Microscopy Identifies Small-Fiber Neuropathy in Subjects With Impaired Glucose Tolerance Who Develop Type 2 Diabetes</title>
+		<author>
+			<persName><forename type="first">Shazli</forename><surname>Azmi</surname></persName>
+		</author>
+		<author>
+			<persName><forename type="first">Maryam</forename><surname>Ferdousi</surname></persName>
+		</author>
+		<author>
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+		</author>
+		<author>
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+		</author>
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+		</author>
+		<author>
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+		</author>
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+	</analytic>
+	<monogr>
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+</biblStruct>
+
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+	<monogr>
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+		</imprint>
+	</monogr>
+</biblStruct>
+
+				</listBibl>
+			</div>
+		</back>
+	</text>
+</TEI>
diff --git a/test.py b/test.py
index 6272e58..b3428e5 100644
--- a/test.py
+++ b/test.py
@@ -2,14 +2,9 @@
 Recursively apply GROBID to the PDF present in a file tree via the grobid client.
 '''
 
-import os
-grobid = __import__('grobid-client')
+import grobid_client as grobid
 
 if __name__ == "__main__":
 
     client = grobid.grobid_client(config_path="./config.json")
-    input_path = "/mnt/data/covid/data/"
-    for root, _, _ in os.walk(input_path):
-        client.process(root, root, 10, "processFulltextDocument", False, 1, 0, True, True, True, False)
-        print(root)
-                
\ No newline at end of file
+    client.process("processFulltextDocument", "./resources/test", consolidate_citations=True, teiCoordinates=True, force=True)