What Motivates Faculty to Adopt Distance Learning

153 Pages·2011·13.2 MB·English

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InternationalJournalonAdvancesinLifeSciences
Volume2,Number3&4,2010
Editor-in-Chief
ElaineLawrence,UniversityofTechnology-Sydney,Australia
EditorialAdvisoryBoard
 EdwardClarkeConley,CardiffUniversitySchoolofMedicine/SchoolofComputerScience,UK
 BerndKraemer,FernUniversitaetinHagen,Germany
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Association,ChiefEditor-TelemedicineandeHealthJournal,USA
EHealthandeTelemedicine
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ElectronicandMobileLearning
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 BerndKramer,FernUniversitatinHagen,Germany
 ElaineLawrence,UniversityofTechnology-Sydney,Australia
 KalogiannakisMichail,UniversityParis5-ReneDescartes,France
 MasayaOkada,ATRKnowledgeScienceLaboratories-Kyoto,Japan
 DemetriosGSampson,UniversityofPiraeus&CERTH,Greece
 SteveWheeler,UniversityofPlymouth,UK
AdvancedKnowledgeRepresentationandProcessing
 FreimutBodendorf,UniversityofErlangen-NuernbergGermany
 BorkaJerman-Blazic,JozefStefanInstitute,Slovenia
 AndrewKusiak,TheUniversityofIowa,USA
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 JeffRiley,Hewlett-PackardAustralia,Australia
 LarsTaxen,LinkopingUniversity-Norrkoping,Sweden
InternationalJournalonAdvancesinLifeSciences
Volume2,Numbers3&4,2010
CONTENTS
AMicro-BiomanipulationTrainingSystembasedonMixed-Reality 73-81
LeonardoMattos,ItalianInstituteofTechnology(IIT),Italy
DarwinCaldwell,ItalianInstituteofTechnology(IIT),Italy
ABiologicallyAccurateSimulationoftheLocomotionofCaenorhabditiselegans 82-93
RogerMailler,UniversityofTulsa,USA
JacobGraves,UniversityofTulsa,USA
NathanWilly,UniversityofTulsa,USA
TrevorSarratt,UniversityofTulsa,USA
Apracticed-basedtechniqueforlearnerstobetterunderstandscholarlyarticles:An 94-102
empiricalstudy
BeebeeChua,UTS,Australia
DaniloBernardo,UTS,Australia
VisualInstrumentGuidanceinMinimallyInvasiveRobotSurgery 103-114
ChristophStaub,TechnicalUniversityMunich,Germany
GiorgioPanin,TechnicalUniversityMunich,Germany
AloisKnoll,TechnicalUniversityMunich,Germany
RobertBauernschmitt,GermanHeartCenterMunich,Germany
ApplyingtheTheoryofConstraintstoHealthTechnologyAssessment 115-124
P.JohanGroop,AaltoUniversity,Finland
KaritaH.Reijonsaari,AaltoUniversity,Finland
PaulM.Lillrank,AaltoUniversity,Finland
NonlinearSpectralTechniquetoAnalyzeWhiteSpotSyndromeVirusInfection 125-132
MarioAlonsoBueno-Ibarra,CentroInterdisciplinariodeInvestigaciónparaelDesarrolloIntegral
Regional(CIIDIR-Sinaloa),México
MaríaCristinaChávez-Sánchez,CentrodeInvestigaciónenAlimentaciónyDesarrolloA.C.(CIAD-
Mazatlán),México
JosuéÁlvarez-Borrego,CentrodeInvestigaciónCientíficaydeEducaciónSuperiordeEnsenada
(CICESE),México
AnAdaptiveEntropicThresholdingTechniqueforImageProcessingandDiagnostic 133-142
AnalysisofMicrocirculationVideos
NazaninMirshahi,VirginiaCommonwealthUniversity,USA
SumeyraDemir,VirginiaCommonwealthUniversity,USA
KevinWard,VirginiaCommonwealthUniversity,USA
RosalynHobson,VirginiaCommonwealthUniversity,USA
RoyaHakimzadeh,SignalProcessingTechnologiesLLC,USA
KayvanNajarian,VirginiaCommonwealthUniversity,USA
ExperiencesandPreferencesofPatientsRegardingaRheumatologyInteractiveHealth 143-153
CommunicationApplication:AqualitativeStudy
RosalievanderVaart,UniversityofTwente,Enschede,TheNetherlands
ConstanceDrossaert,UniversityofTwente,Enschede,TheNetherlands
ErikTaal,UniversityofTwente,Enschede,TheNetherlands
MartvandeLaar,UniversityofTwente,MedischSpectrumTwente,Enschede,TheNetherlands
LearningContextsasEcologiesofResources:AUnifyingApproachtothe 154-164
InterdisciplinaryDevelopmentofTechnologyRichLearningActivities
RosemaryLuckin,TheLondonKnowledgeLabTheInstituteofEducationLondon,UK
Africa’sTelenursingToday(andTomorrow?) 165-172
SinclairWynchank,MRC,SouthAfrica
JillFortuin,MRC,SouthAfrica
WhatMotivatesFacultytoAdoptDistanceLearning?DataCollectedfromaFaculty 173-187
DevelopmentWorkshopCalled“BuildaWebCourse”
TamaraMichelePowell,KennesawStateUniversity,USA
AProcessModelforEstablishmentofKnowledge-BasedOnlineControlofEnterprise 188-199
ProcessesinManufacturing
DanielMetz,UniversityofSiegen,Germany
SachinKaradgi,UniversityofSiegen,Germany
ManfredGrauer,UniversityofSiegen,Germany
SaliencyDetectionMakingUseofHumanVisualPerceptionModelling 200-208
CristinaOprea,PolitehnicaUniversityofBucharest,Romania
ConstantinPaleologu,PolitehnicaUniversityofBucharest,Romania
IonutPirnog,PolitehnicaUniversityofBucharest,Romania
MihneaUdrea,PolitehnicaUniversityofBucharest,Romania
Core-BodyTemperatureAcquisitionToolsforLong-termMonitoringandAnalysis 209-218
JoãoM.L.P.Caldeira,UniversityofBeiraInterior,Portugal
JoelJ.P.C.Rodrigues,UniversityofBeiraInterior,Portugal
JoséA.F.Moutinho,UniversityofBeiraInterior,Portugal
MarcGilg,UniversityofHauteAlsace,France
PascalLorenz,UniversityofHauteAlsace,France
InternationalJournalonAdvancesinLifeSciences,vol2no3&4,year2010,http://www.iariajournals.org/life_sciences/
73
A Micro-Biomanipulation Training System based on Mixed-Reality

Leonardo S. Mattos, Darwin G. Caldwell
Advanced Robotics Department
Italian Institute of Technology (IIT)
Genoa, Italy
{leonardo.demattos, darwin.caldwell}@iit.it

Abstract— Within neuroscience, micromanipulation and motion capabilities are difficult to achieve and do not easily
microinjection of cells (blastocysts and neurons) are essential translate into accurate control and successful manipulations.
and highly skilled tasks that can require years of training. As a result of these operational difficulties the training
These tasks are traditionally performed via direct manual period required to achieve proficiency in biomanipulations
control of the biomanipulation equipment while looking is normally high, reaching up to one year for operations
through a microscope. Yet, even after extensive training, yield such as embryo microinjection [2]. In addition, the training
can be low (40 – 70%). This paper presents a mixed-reality
process is expensive, involving not only the costs of the
system for the training of operators (biologists/neuroscientists)
underperforming operator, but also expenses associated with
on a new fully teleoperated biomanipulation system with
wasted materials, samples preparation, cell culture, etc.
reduced training requirements and much higher yields. Two
Furthermore, even after extensive training, the success rates
mixed-reality training scenarios were designed, implemented
of biomanipulations are found to be less than ideal (40 -
and tested for this purpose: A “move-and-inject” task focused
70% for embryo microinjections [3]), pointing to problems
on precise positioning training; and a trajectory following
related to the user interface and ergonomic factors of the
scenario intended to develop precise motion control skills in
microscope/micromanipulation setup [4]. Typical issues
new operators. Preliminary experiments performed with 20
totally novice operators demonstrate that this new training include: high susceptibility to human errors, such as
system is effective in terms of the initial development of control unintentional erroneous motions; and the tiring working
skills for real teleoperated biomanipulations. Experimental conditions, where operators spend hours looking through
metrics demonstrate an exponential learning curve for these microscopes while simultaneously controlling
novice operators, who achieve good performance values after micromanipulators and microinjectors.
only two practice runs on the system. In addition, this training Improvements to the biomanipulation setup and to its
is shown to be safe and inexpensive since no real cells, control interface can be achieved using teleoperation
biochemical products, or several pipettes are needed for this techniques whereby the operator controls the system from a
initial training phase. computer station, looking at the live video captured from the
microscope and displayed on the computer screen. The
Keywords-mixed-reality; teleoperation; biomanipulation; control of the micromanipulators can be accomplished
micromanipulation through the computer keyboard [5]; game joysticks [6]; or
even through haptic devices [7][8].
A teleoperated system has the potential to greatly
I. INTRODUCTION
improve biomanipulations by offering supervised control of
Biomanipulation, in the context of this work, involves the micromanipulator motions, which can filter hand
the transportation, orientation and injection of microscopic tremors and even block erroneous motions [9][10]. In
biological structures such as single cells and early embryos. addition, a single teleoperated system can offer different
These operations are normally performed under high- control modalities for the micromanipulators, including
magnification microscopes using glass pipettes with very position [11], velocity [6] and force control [12], which can
fine tips (2-50µm), which are attached to micromanipulators be selected according to the specific biomanipulation task or
and microinjectors. Micromanipulators are mechanical or user preference. Furthermore, the speed and precision of the
electro-mechanical devices that scale down the operator’s micromanipulator can be dynamically adjusted in a
motions to enable precise control of tools in the micrometer teleoperated system, both automatically [13][14] or
range. Microinjectors are devices used for precise control manually, further improving manual operations.
of fluid motion inside the pipettes. Additional benefits of a teleoperated biomanipulation
Modern biomanipulation devices are motorized and system include the automatic execution of motions that are
capable of high-resolution control. For example, commonly virtually impossible under direct manual control of the
used Eppendorf equipment, such as the TransferMan NK2 micromanipulators. Examples are simultaneous motions in
micromanipulator and the FemtoJet microinjector, offer three dimensions, e.g. fast and precise motions such as
motion resolution down to 40ηm and the capability of “stabbing” movements used to penetrate some cells; and
injecting volumes down to the femtolitre into cells. slow linear motions often desired for retracting the pipette
However, under direct operator control these high-resolution from injected cells along the entry path. Another obvious
2010,©Copyrightbyauthors,PublishedunderagreementwithIARIA-www.iaria.org
InternationalJournalonAdvancesinLifeSciences,vol2no3&4,year2010,http://www.iariajournals.org/life_sciences/
74

(a) (b)
Fig.1. (a) Microinjection of CHO-K1 cells under bright-field imaging. (b) Microinjection of neurons using phase contrast imaging. Scale bars on the pictures
read 10µm. The diminutive size of these adherent cells require the use of high-magnification objectives, complicating precise pipette positioning over the
target cells due to the shallow focal depth of these lenses.
benefit is the creation of a more ergonomic and intuitive fully teleoperated biomanipulation system [6] was used as
work environment by careful arrangement of the display the real setting for operator training, and target cells were
screen and the interfaces to enhance hand-eye collocation replaced by virtual targets and virtual obstacles. Training
and co-ordination. This is less tiring, since the operator can was performed in two different mixed-reality scenarios: A
sit comfortably in front of a computer display. In fact, this is move-and-inject task focused on training precise pipette
a familiar environment to most people, including new positioning; and a trajectory following scenario, intended to
operators that should learn biomanipulation tasks, making train operators on precise control of the pipette motions.
us hypothesize that teleoperation can enable faster learning Evaluation of training metrics from 20 totally novice
at least in part due to this simple reason. operators are presented here, showing that user learning
Mixed-reality systems have been developed and used in improved exponentially and demonstrating the great value
teleoperations to achieve a diverse range of goals. For of this mixed-reality training system.
instance, they have been used to increase the safety of
operations through the creation of virtual barriers in the II. BIOMANIPULATION TASKS
operating field [10][15]; and also to assist in cell Within the broad field of biomanipulation and
microinjection tasks by providing a preferred direction of particularly microinjection, which is the primary focus of
motion [3]. Mixed reality has also been used in predictive this research, two tasks are of special interest due to their
displays, assisting the planning and execution of robot importance and frequency in neuroscience research:
motions [16]; and in nanomanipulations to provide “haptic” adherent cell microinjections and blastocyst microinjections.
sense in intangible tasks [17]. They have also been used in Adherent cell microinjection is a delicate and
many training systems for tasks varying from aircraft complicated operation that involves the manipulation of
piloting [18] to minimally invasive surgery [19]. cells with dimensions down to 10µm. Figure 1(a) shows
The use of an assistive mixed-reality system can also adherent cells commonly used in biological and medical
improve the training and the yield of biomanipulations. For
example, our teleoperated system [6] allows the definition
of operating zones directly on the live video display, which
are used to dynamically adjust the speed and precision of
pipette
the micromanipulator during operation. This is useful to
prevent errors and contributes to increased operation yields Z θ
because the micromanipulator precision can be
X
automatically increased when the biomanipulation pipette is
near a target cell or a danger area.
This paper focuses on the training benefits of a mixed- cell
reality biomanipulation system. Our goal is to show that θ Petri dish
initial operator training can be done “off-line,” without the
need for real cells, biochemical products, or the numerous
pipettes that are needed when learning biomanipulations on Fig. 2. Synchronized motions on the X- and Z-axis are necessary to create a
the traditional manually controlled systems. To this end, a linear microinjection action when the pipette is positioned on an angle.
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InternationalJournalonAdvancesinLifeSciences,vol2no3&4,year2010,http://www.iariajournals.org/life_sciences/
75

Fig. 3. Blastocyst microinjection and high-level algorithm describing this delicate operation. Coordinated and meticulous control of two micromanipulators
and two microinjectors is required in this case, making this operation difficult to master and characterizing its results by low success rates.
research, the CHO-K1 cells [20]. Other examples are Nonetheless, efficient operation of this new system also
neurons, as in Fig.1(b), and endothelial cells. Since these requires some training to attain optimum performance,
cells adhere to the bottom of the Petri dish, only one which motivated the development of the mixed-reality
pipette/micromanipulator is required to perform the training system described in this paper. The next sections
microinjections. However, the pipette needs to be lifted present the system created, the training procedure, and
when moving from one cell to another to avoid collisions initial training experiments performed with completely
with other cells and pipette tip breakage. Then, the pipette novice operators.
should be precisely positioned on the upper surface of the
next target cell. This operation is complicated by the fact III. BIOMANIPULATION SYSTEM CONFIGURATION
that the diminutive cells require the use of microscope The teleoperated biomanipulation system used in this
objectives with high magnification factors (40 - 100x), research was created by the integration of high-end
which have a shallow focal depth. This means the pipette tip commercial equipment commonly found in neuroscience
goes completely out of focus when lifted, making precise research laboratories. This was done in favor of: 1) creating
positioning difficult. In addition, the injection motion a flexible system applicable to a large range of
involves simultaneous and coordinated motion in two axes biomanipulation applications; 2) minimizing extra
when 3-axis micromanipulators are used, e.g., injection at investment from the laboratories that already possess
angle from the top involves simultaneous motions in X and biomanipulation equipment; and 3) increasing the system’s
Z (see Fig. 2). acceptance by the biology/neuroscience community, which
Blastocyst microinjections can be considered as a form is already familiar with and trusts the equipment used.
of suspension cell microinjection, even though the Equipment selection and configuration was performed in
blastocysts (early embryos) are composed of many cells. In collaboration with neuroscience researchers. Based on the
this case, the (mouse) blastocyst moves freely on the bottom
mix of their research needs with engineering specifications
of the Petri dish, so injection normally requires the use of
for automation, the developed system included:
two moveable pipettes: one to hold the blastocyst; and the
second to perform the actual injection. Figure 3 shows this • one Leica DMI6000B inverted microscope
operation, from which it can be seem that mice blastocysts, • two Eppendorf TransferMan NK2 motorized
measuring around 100µm in diameter, are much larger than micromanipulators
the adherent cells mentioned above. Nonetheless, blastocyst
• one Eppendorf Femtojet microinjector
microinjections are equally delicate and difficult since even
• one Marzhauser SCAN IM 120x100 motorized
a small error can kill the embryo. One complication here
scanning microscope stage
comes from the fact that this task requires coordinated
• two Eppendorf CellTram Vario microinjectors
control of two micromanipulators; and another from the
incorporating custom computer-controlled driving
need to also control two microinjectors. These
complications contribute to increasing the training time systems
required to attain proficiency on this operation, which can • a desktop computer with an Intel Core2 Quad 2.83
typically take up to one year. GHz CPU, WindowsXP, and 3GB RAM
As a result of the long training periods and susceptibility • an AVT Guppy F-080C firewire camera
to small errors, our research aims at increasing the • two Saitek Cyborg Evo Force joysticks
consistency and efficiency rates attained in manually
controlled operations through assisted teleoperation. These devices are shown in Fig. 4, which presents the
Consequently, we have developed a teleoperated system that two workstations that constitute the developed teleoperated
allows easy and precise control of the entire biomanipulation system: The microscope station and the
biomanipulation setup from a user-friendly interface. control station.
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76

(a) (b)
Fig. 4. Teleoperated biomanipulation system configuration: (a) the microscope station; (b) the computer/control station.
The selected devices are appropriate for many different interface (GUI) running on the desktop computer (see Fig.
biomanipulation procedures, including the microinjection of 5). User commands were received via the joysticks,
blastocysts and adherent cells. These operations can be computer mouse or computer keyboard. These commands
precisely performed using the selected micromanipulators were processed and then forwarded to the appropriate
(which have three translation axes with resolution of 40ηm biomanipulation equipment.
Feedback from the micro-world was obtained through the
and a maximum speed of 7.5 mm/s) and the FemtoJet
video camera, which provided live video feed from the
microinjector, which can deliver volumes down to the
microscope’s field of view.
femtolitre.
The virtual features used during this research were
IV. SYSTEM CONTROL AND TELEOPERATION created using graphics overlaid on the live video stream. The
interaction of these virtual features with real system
Control and teleoperation of the devices were achieved
components created the mixed-reality biomanipulation
through standard 2-way communication interfaces,
environment. These interactions were enabled by mapping
including RS-232C and CAN. This was possible because
the micromanipulator coordinates to the image space, as
all of the selected devices supported external control described in [21] and summarized below. Using this
through a serial communications port. Therefore, the type mapping the system was able to compute the image
of connection between the controlling computer and a coordinates of the tool (pipette) without the need for image-
system device was dictated by the device’s supported based localization software. This created a robust and fast
interface. system capable of generating an operating environment
All system devices were integrated by software, and influenced by both real and virtual objects. Figure 5 shows
could be simultaneously controlled from a graphical user some of the virtual features that could be created, including;
Targets
Obstacles Commands
Commands
Home region
Danger region
Feedback
Fig. 5.The mixed-reality biomanipulation system setup and examples of virtual features defined on the operating environment.
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