Atomic force microscopy is a powerful topography imaging method used widely in nanoscale metrology and manipulation. A conventional Atomic Force Microscope (AFM) utilizes an optical lever system typically composed of a laser source, lenses and a four quadrant photodetector to amplify and measure the deflection of the cantilever probe. This optical method for deflection sensing limits the capability of AFM to obtaining images in transparent environments only. In addition, tapping mode imaging in liquid environments with transparent sample chamber can be difficult for laser-probe alignment due to multiple different refraction indices of materials. Spurious structure resonance can be excited from piezo actuator excitation. Photothermal actuation resolves the resonance confusion but makes optical setup more complicated. In this paper, we present the design and fabrication method of coated active scanning probes with piezoresistive deflection sensing, thermomechanical actuation and thin photoresist polymer surface coating. The newly developed probes are capable of conducting topography imaging in opaque liquids without the need of an optical system. The selected coating can withstand harsh chemical environments with high acidity . The probes are operated in various opaque liquid environments with a custom designed AFM system to demonstrate the imaging performance. The development of coated active probes opens up possibilities for observing samples in their native environments. An Atomic Force Microscope (AFM) is a nano-scale topography imaging tool widely used in high-tech industries and nanotechnology researches . AFMs caConventional AFMs utilize an Optical Beam Deflection (OBD) system to amplify the cantilever probe deflection caused by the nano-scale topography variation. In this setup, a beam of monochromatic coherent light, typically produced by a laser source with mode hopping compensation circuitry, is focused on the reflective back of the cantilever probe at an angle. The small deflection of the cantilever probe tip causes a small change in relative angle between the incident light and reflective probe back. The optical path amplifies this change to a larger movement and a four quadrant photodetector is used to convert it into an electrical signal for data acquisition systems to process. Successful implementation of the OBD system allows low-noise sub-nanometer resolution for topography measurement. With a typical size on the order of tens of microns, the laser spot size can be further reduced with high numerical aperture objective lens to work with smaller cantilever probes  in high-On the other hand, opaque liquid environment has practical importance in many areas of research. For example, in the refinery industry, crude oil is produced from a porous network of minerals  with mixture of oil and water. In this case, the wettability of the mineral determines the fluid distribution. This in turn affects the flow transport in crude oil reservoirs . As crudWith the development of micro-machining and lithography technology for integrated circuit manufacturing, active cantilever probes with self-sensing and actuation can be fabricated. For deflection sensing, a piezo-resistive element embedded at the base of the cantilever probe can be connected with a circuit to provide a voltage signal proportional to the probe tip displacement. Actuation of the probe for dynamic mode operation can be achieved mechanically by conventional piezo actuators, photothermally by laser or thermomechanically with built-in heating elements. Active cantilever probes have demonstrated their capability for tapping mode AFM imaging. However, the functional components on the active cantilever probes can be electrically shorted or mechanically damaged by corrosive chemicals. If proper protective coating can be applied to the active probe without severely altering its functionality, coated active cantilever probes can be a promising technology to enable opaque liquid imaging for AFM systems.In addition to imaging environment, size of the imaging area, spatial resolution and imaging speed (line rate) are all important considerations for the design of an AFM imaging system. A versatile imaging system is needed for various scenarios. Previous research efforts have focused on improving imaging speed of conventional AFM system by designing new generation scanners , utiliziIn this work, we present the design and application of coated active cantilever probes for topography imaging in non-transparent liquid environment with a custom built versatile AFM system. The paper is organized as follows. First, we introduce the design, fabrication, coating and characterization of the active cantilever probe. Second, we present the overall design and instrumentation of the custom imaging system capable of large range overview and high-speed local imaging. Third, we provide topography imaging results obtained from various opaque liquid environments with coated active cantilever probes in tapping mode. Finally, we discuss the potential improvements for the coating of active probes for better imaging performance.The key to enable opaque liquid environment imaging is a coated active probe. The design and manufacturing of an active probe with nano-fabrication technology is an intricate process that requires significant investment of time, capital and effort. At such cost, the developed active probes offer a number of benefits. First, the removal of the OBD system for deflection sensing enables easy exchange of probes without the need of laser alignment and reduces the size of the system. At such a size an AFM can even be integrated into a Scanning Electron Microscope (SEM) system . Second,Conventional AFM probes are etched from silicon wafers. Different choices of material, sizing and specialized probe tip treatment can be designed for different applications. In addition to the cantilever, there are two major functional subsystems embedded into our active probe, namely, (1) a piezoresistive stress sensor for probe deflection measurement and (2) thermomechanical actuator. A schematic illustration and an SEM image of the uncoated active probe is shown in For cantilever deflection sensing, p-doped piezoresistors are placed at high stress locations for bending. In the case of a multi-layer cantilever, the displacement is related to the stress between layers of maximum thermal expansion coefficient difference . The mecThermomechanical actuation is achieved by resistive heating of the cantilever formed by multiple layers with different coefficients of thermal expansion. To be specific, a  derived . For driFabrication of the cantilever probe involves multiple lithography steps on an N-type silicon substrate. The tip of the probe is formed first and then the piezoresistor deflection sensing element with boron diffusion. After that, the heating element is formed by metal etching of an aluminum deposition layer masked with photoresist. Other types of alloys such as Cu/Au/Ta alloy with high expansion coefficient can also be utilized. A bonding process attaches the cantilever probe onto an SD card shaped PCB for electrical contact and easy probe exchange. A detailed step-by-step illustration of the cantilever fabrication process can be found in reference .Bond well to the surface material of the cantilever probe .Be chemically inactive for selected environments depending on the application .An uncoated active probe is suitable for operation in ambient and vacuum environments. For operation in liquid environments, it is necessary to protect the probe with coatings for electric insulation and resistance to corrosive chemicals. The coating should satisfy several requirements as discussed below to insure that it does not compromise the functionality of any subsystem of the active probe.Due to the delicate nature of the active cantilever probe and complication in redesigning the manufacturing process, dip coating of the active probe is selected as the fabrication method for trying out different materials for coating. In this work, various types of polymers have been evaluated including M-Bond 610, 2K.-Epoxy, M-Bond 43B and photoresist polymers for photolithography. Most of these tested materials except “Positiv 20” suffer from poor bonding, thick coating layers, inability to withstand corrosive chemicals or failure to maintain a usable resonance peak for the probe. Before the coating of the MEMS cantilever, we first protected the bond wires connecting the silicon chip with the carrier PCB. For this we used a viscous two-compound epoxy glue EPO-TEK H70E, which itself is not suitable to coat the actual cantilever beam. The epoxy was cured at 100 To better understand the effect of coating, frequency response experiments were conducted for the cantilever probes before and after coating, The results are shown in After the coating application, the overall mass of the probe increases, which should in principle reduce the resonance frequency of the probe. However, all probes after coating demonstrated an increase in the first mode resonance frequency. This indicates the coating increases the stiffness of the cantilever probe, which results in an overall increase of resonance frequency. The oscillation amplitude of the probe also decreased to be almost an order of magnitude smaller than the original uncoated probe with identical driving signals. This can partly be attributed to the increase of probe stiffness or damping. Another possible reason is that the coating increases the thermal capacitance and resistance of the probe such that the temperature variation amplitude is decreased. This results in a smaller probe deflection that is measured from the frequency sweep. The decrease of oscillation gain amplitude can partly be compensated by increasing the driving signal but might result in a reduced operation lifetime of the probe due to extensive heating and increased internal stress.To evaluate the imaging performance of the coated active probes, a custom AFM system has been designed. The setup incorporates both active probe and OBD system passive probe imaging with a sample scan configuration. An overview of the AFM system Solidworks design and picture of the assembled setup is shown in To cater to various types of research demands, a 5-axis scanner with multi-actuation is designed. For large range overview of the sample, a P-611.XYZ from Physik Instrumente (PI) is utilized. The scanner operates in 3 orthogonal axes at 100 eference . High-spThe high-precision engagement system is constructed by combining a PK523HPA-H50S harmonic drive stepper motor from Oriental Motor Corporation capable of 25,000 steps per revolution with a DS40-XYZ positioner with 80 revolution per inch accuracy. The combined setup has a theoretical 12.7 nanometer step size that allows precise control of the engagement process. The active probe holder includes mounting structure and signal conditioning circuitry. The optical system utilizes infinite corrected objective lens to allow optical microscope view and optical readout for passive probes. External drivers with piezo actuator hysteresis compensation capability to improve positioning accuracy without usage of feedback sensor  are alsoT is the temperature, R is the heater electrical resistance, t is time. We implicitly utilized the trigonometry identity Due to the thermomechanical actuation of the active probes, the driving method is different from conventional tapping mode AFM imaging with passive probes. The relationship between the deflection of the probe and input voltage with DC and AC components is given in Equation .1)Up∝δUp∝δ(1)UpTo improve the demodulation speed, integration over integer number of periods is preferred over long time integration with low pass filter. Based on previous research, the oscillation amplitude can be extracted with only a few periods of the sinusoidal waveform . With prFor implementation, the lock-in amplifier is realized with NI LabVIEW on an PXIe-7975R FPGA board with NI-5782 high bandwidth digital IO at 125 MHz sampling rate. Special care is taken to minimize the FPGA fixed point arithmetic quantization error for precise integer periods integration. The high-speed PXIe-7975R FPGA processes data from the active probe readout circuitry and provides the lock-in amplifier output to the PXIe-7868R FPGA, which handles the scanning pattern generation, regulation and image data acquisition. An event driven state machine is implemented in real time for user interface event handling, controller tuning and FPGA resource coordination. Real time 2D/3D topography visualization and recording is also implemented for user convenience considerations.In this section, we provide imaging results in various environments with the coated active probes. For consideration of signal quality and the bluntness of the active probe tip due to coating, amplitude regulation tapping mode is more suitable than contact mode for better imaging resolution. We evaluated the imaging performance of coated active probe comparatively with uncoated active probe and investigated the functionality of each in different chemical and biological environments. With tapping mode operation and cosidering feature size of the sample, we utilized the large range scanner to cover areas of 6 by 6 micron for oil imaging and 12 by 12 micron for acid imaging respectively.To evaluate the capability of the coated cantilever probe and obtain a better understanding of liquid environment imaging performance, the same sample was imaged with uncoated active probe in air, coated active probe in air and coated active probe in crude oil. The results are shown in As can be seen in To investigate the capability of the coated probes to resist corrosive environments, acid environment imaging was conducted with a larger sample height variation. In this case, vinegar with black color with pH value around 3 was used. Sample setup of a TGZ-500 calibration grating, imaging result and visualization of probe coating changes after imaging are shown in The coated active probe performs well compared to the uncoated probe despite its relatively smaller oscillation amplitude and sample’s large step height variation. Despite the visible change of the coated probe back surface as shown in Immersion test for whole blood sample with Ethylenediaminetetraacetic acid (EDTA) anti-coagulation treatment from AllCells LLC was also conducted. The coated probe worked well at the beginning of the immersion in terms of resonance identification. However, due to the difference in densities of its components, the blood sample formed a deposition of multiple layers. In addition, the presence of heterogeneous floating cells affected the resonance frequency of the probe. These complications made the probe engagement with the sample surface a time consuming task. Furthermore, even with anti-coagulation treatment, the blood sample solidified within approximately 30 min of exposure to ambient air by forming an outer shell at the air-blood interface. This phenomenon significantly affects the resonance of the probe and can even break the active probe. Hence, the coated probe is functional in a whole blood sample but effective imaging will require specialized treatment with chemical agents or dedicated environmental control to prevent blood sample solidification.A number of observations were made during the imaging process that are each worth further discussion. In this section, we share our experiences with the AFM imaging of opaque liquids using coated active probes. The report will also include observations of probe coatings after extended time of usage. Potential improvements of the coating for better imaging performance are also discussed.A number of issues can arise when operating in liquid environments. One of these is that upon moving the probe from a gas (air) into a liquid environment, its resonance frequency often shifts due to added mass of the liquid. This shift can be confused for engagement with the sample for reduced oscillation amplitude. Several other resonance frequency shifts occur at the end of the engagement upon approaching the solid surface. Hence, several cantilever frequency sweeps need to be conducted to ensure the actual surface of the sample is reached.Second, depending on the density and viscosity of the liquid, the first resonance frequency can become significantly lower such that the amplitude demodulation speed is too slow. In this case, second mode of the resonance frequency can be utilized for imaging.Finally, due to the surface tension and wetting effects of liquids, residuals of the liquid remain on the polymer coating of the probe after experiment. After drying in air, the residual particles can change the mechanical properties of the probe or react with liquid in the next experiment. Therefore, it is necessary to clean the probe in chemical solution to dissolve residual particles. The probe needs to be rinsed in deionized water and then dried in a clean environment to avoid dust attachment.In addition to the increase of probe mass, stiffness and resonance frequency as demonstrated in By applying protective coating to the active cantilever probe, the new capability of harsh opaque liquid imaging for AFM system is enabled. As demonstrated by the imaging performance, the side effects of reduced probe sharpness and non-linearity due to thermomechanical stress induced fatigue did not reduce the usefulness of such technique for tapping mode imaging. We now discuss possible improvements and potential methods. First, the thickness of the coating should be reduced for smaller added mass. Second, the overall probe stiffness increase caused by the coating should be minimized to maintain original probe oscillation amplitude for imaging sensitivity considerations. Third, bonding strength of the coating with the probe should be improved to sustain long time operation without added non-linearity. Last but not least, increasing the overall coated area to cover the entire electronics area to allow deeper immersion into the liquid environment would be helpful for experiment purpose.Vapor deposition techniques would be an ideal future method of adding the coating because it offers good bonding and atomic level thickness control. However, the development of such coating requires augmentation of the nano-fabrication process. This will require significant investment of time and capital for development. Another promising potential method is to apply parylene coating, which allows control of coating thickness below one micron. Parylene coating can also resist harsh chemical environments and be applied after probe fabrication with current design.In this work, we extended the capability of AFM systems to operate in opaque liquid environments. The design, fabrication and coating of active cantilever probes are presented. A versatile AFM system has been custom designed for operation in various range, bandwidth and probe configurations. Coating and system performance are verified with imaging in vinegar, crude oil, and immersion test in blood sample. Experiment key points and potential improvements are further discussed for usage of the cantilever probe. For future work, the authors will continue experimenting on other coating methods to improve the performance of coated active probes for AFM imaging in chemically harsh opaque environments.A provisional patent has been filed for the results of this research.


In the past 20 years, the concept of surgery has undergone profound changes. Surgical practice has shifted from emphasizing the complete elimination of lesions to achieving optimal rehabilitation in patients. Collaborative optimization of surgery consists of three core elements, removal of lesions, organ protection and injury close monitoring, and controlled surgical intervention. As a result, the traditional surgical paradigm has quietly transformed into a modern precision surgical paradigm. In this review, we summarized the latest breakthroughs and applications of precision medicine in liver surgery. In addition, we also outlined the progresses that have been made in precision liver surgery, the opportunities and challenges that may encountered in the future. Since Langenbuch implemented the world's first successful liver resection in 1888, liver surgery has developed tremendously Precision medicine (PM) is a novel medical model that was first proposed in 2011 by the National Research Council of the United States in a book named “Towards Precision Medicine: Building a Knowledge Network for New Taxonomy in Biomedicine and Disease”In 2006, Professor Dong first proposed the concept of “precision liver surgery” to the medical world As digital age is approaching, hepatectomy is undergoing a third life science revolution. Advancements in medical technology have contributed to the development of new techniques represented by artificial intelligence such as imaging omics, three-dimensional (3D) visualization and printing, molecular fluorescence imaging, multi-modal image real-time surgical navigation, etc. The trend of applying digital intelligent for diagnosis and treatment is approaching. This review intends to introduce the techniques using intelligent digital in hepatectomy from the following aspects: 1. Medical big data and artificial intelligence in the diagnosis of liver tumors; 2. 3D visualization, virtual simulation surgery and 3D printing technology to guide preoperative evaluation and surgical mode selection; 3. Molecular fluorescence imaging technology and virtual reality technology to navigate during surgery to achieve precise resection of the lesion; 4. Predictive models to guide post-operative management and the application of wearable devices as accelerated approaches to post-operative rehabilitation.With the aid of medical science and technology, medical data has been growing at an unprecedented rate since the late 2000s. On January 28, 2016, former President Obama signed a presidential memorandum to launch the White House Cancer Moonshot, a program to increase awareness of the importance in accelerating progress towards cancer prevention and treatment. The program emphasizes the importance of breaking down the data barriers to create a comprehensive and effective global cancer knowledge network that can accelerate the integration of genomics, epidemiology and clinical information to enable precise treatment of cancer. The completion of the Human Genome Project and the production of massive molecular biology data such as proteomics, transcriptomes, and metabolomes are the cornerstones of precision medicine DGE is currently applied to the basic hepatic carcinoma molecular research. Although the application of DGE has been making progressions in the research field, it has low clinical conversion rate. To overcome this obstacle, we should combine these high-throughput genomics, proteomics, and metabolomics analyses with high-quality and completed clinical information. In part to increase the number of potential therapeutic targets, investigators from The Cancer Genome Atlas (TCGA) Research Network identified genomic alterations in 196 tumors that influence development of HCC, including mutations in the TERT gene promotor, mutations in the TP53 and CTNNB1 (β-catenin) genes, and elevated expression of several immune checkpoint genes In April 2018, China National Liver Cancer Center launched a multi-center, prospective liver cancer screening program named PreCar. The project is planned to establish a follow-up cohort study, with 1-1.5 million high-risk populations of liver cancer and conduct a three-year prospective cohort study. Early detection serum markers for liver cancer will be screened, identified and applied into clinical use by using mature high-throughput genome sequencing techniques. Optimizing early liver cancer treatment strategies through big data results and provides high-quality evidence-based medicine, spares patients from suffering complex and expensive care later. While exploring the molecular mechanisms and driving genes of liver tumors, all results will be shared to the clinical. With the guidance of gene mutations biomarkers, we can apply the most appropriate treatments such as chemotherapy, radiotherapy, targeted therapy, and immunotherapy to individual patients.The advent of the era of big data has enabled a large amount of untapped medical materials to be available for quantitative analysis such as surgical video. At present, a large number of surgical videos can be quantitatively analyzed through machine learning, using computer vision principles to achieve automatic, real-time analysis and segmentation of surgical procedures. Sharing these videos with other surgeons can also help to improve their surgical skills or for educational purposes Artificial intelligence is a new technology that develops theories, methodologies, and applications for simulating and extending human intelligence. The application of artificial intelligence in medical field allows computer algorithms to recognize patterns, approximate conclusions, and suggest treatments Machine learning, a system that can learn, identify and make decisions from data, is a current application that supports artificial intelligence. Deep learning is a subset of machine learning, it is capable to establish and imitate the neural network of human brain for analysis and learning. The mechanism of imitating human brain learns the data automatically features in multi-layer structure and improves the accuracy of classification and prediction. Accurate diagnosis is an important factor in the implementation of precision medicine. Artificial intelligence-based Radiomics research is currently the most discussed research topic. With the development of image recognition technology, artificial intelligence technology can extract the subtle texture features in images, predict the pathological features of microvascular invasion, neurological invasion, lymph node metastasis and other prognosis before surgery, and guide individualized treatment. Furthermore, artificial intelligence technology can help to identify benign and malignant liver tumors, diagnose rare pathological types of liver tumors, and guide clinical diagnosis and treatment In 2012, Dutch scholar Lambin et al. first proposed the concept of Radiomics Pathology is the gold standard for tumor diagnosis. Due to the development of full-glass digital scanning system, data storage capacity has increased the advancement of graphic recognition technology, pathological slice recognition and evaluation based on artificial intelligence technology has a high accuracy The 3D visualization technique of liver tumors refers to a tool for displaying, describing and interpreting the 3D anatomical and morphological features of liver tumors. Previous clinical imaging of liver tumors relied mainly on computer image processing technology such as B-ultrasound, CT and/or MRI, etc. This computer image data has been used to analyze and calculate data, and described the morphology and spatial distribution of targets such as liver, biliary tract, blood vessels and tumors In addition to obtaining basic diagnostic information such as CT and MRI, liver surgeons also need to recognize the local lesion itself and its adjacent organ details based on individual patient. Prior to the operation, detailed planning of the scope of the lesion and surgical procedure should be made to ensure a safe operation Erbay et al. found that intrahepatic vascular variability was as high as 70% 0 grade: tumor does not compress the portal vein;st grade: tumor compression but does not invade the portal vein;1nd grade: tumor invades the portal vein trunk, but the continuity of the blood vessels is uninterrupted;2rd grade: tumor invades the portal vein trunk and interrupts the continuity of blood vessels.3The degree of portal vein involvement and variation has a decisive influence on the choice of surgical approach for hilar cholangiocarcinoma. The portal vein separation limit point under normal conditions  refers to the bile duct in the hepatectomy from the parallel portal vein and the extreme part of the hepatic artery that is peeled off. The bile duct beyond this limit point cannot be separated and must be cut off separately. The portal vein separation limit point under normal conditions refers to the extreme part of the bile duct that can be separated from the parallel portal vein and hepatic artery during hepatectomy . The bile duct beyond this limit cannot be separated and must cut off separately. In the case of portal vein variation, the portal vein separation limit point will move forward or downward. It is extremely important to identify the variation of the intravascular vascular structure to determine the exact location of the hepatic resection limit point. Based on the 3D reconstruction of portal vein, we propose to classify the adjacent relations between tumor and portal vein into 4 grades:The portal vein of patients with grade 0 or 1 has not been violated; patients with grade 2 or 3 require combined revascularization to achieve radical resection. The concept of vascular assessment centered in the portal vein is different from the concept of grading the portal vein and superior mesenteric vein proposed in laparoscopic pancreaticoduodenectomy. The latter is similar to the joint vascular assessment and exploration perspective, emphasizing the procedural and standardization of the surgical procedure. The portal vein is used as the axial center combined with the portal vein separation limit points  to evaluate the portal vein condition, emphasizing the development of individualized and radical surgery strategies for hilar cholangiocarcinoma Optical imaging technology represented by near-infrared fluorescence imaging technology has been applied to liver surgery, providing technical support for real-time, high-precision surgical navigation. Indocyanine green (ICG) is a cyanine dye used in medical diagnostics. It is used to determine cardiac output, hepatic function, liver and gastric blood flow, and for ophthalmic angiography. These infrared frequencies penetrate retinal layers, allowing ICG angiography to image deeper patterns of circulation than fluorescein angiography. ICG binds tightly to plasma proteins and becomes confined to the vascular system. ICG has a half-life of 150 to 180 seconds and is removed from circulation exclusively by the liver in the form of bile juice.ICG-mediated near-infrared light detection technology is widely used in surgical navigation However, due to problems such as shallow penetration depth, tissue interference from fluorescent background, and poor imaging specificity, the application of fluorescent optical imaging in medicine is limited. However, a new technology was developed to overcome this limitation. Optical/ acoustic multimode technology, a system where it combines photoacoustic imaging technology with traditional ultrasound technology to enable high-precision imaging across molecules, cells, tissues, and organs. It can also quantitatively analyze the blood flow and oxygen metabolism of the lesion, and provide conditions for accurately determining the tumor boundary and guiding the accurate liver tumor resection.The anatomical structure of the hilar is complicated. Therefore, the 3D spatial relationship between the departmental vein, hepatic artery, hepatic vein and tumor in the liver of patients with biliary tract tumor is obtained and this information is the basis of surgical treatment for biliary malignant tumor. In liver surgery, augmented reality technology uses CT and MRI data to reconstruct a 3D image of the liver and intrahepatic vasculature for surgical planning, where the virtual image is superimposed in the surgical field for navigation. The 3D image of the surgical plan is superimposed on the liver surface in a 1:1 ratio, which can achieve the preliminary definition of the liver range of pre-excision of gallbladder cancer or hilar cholangiocarcinoma, and help the surgeon to achieve accurate liver resection. Laparoscopic surgery is the direction of biliary tumor surgery. For laparoscopic liver resection, in addition to tactile feedback, visual feedback is also critical for fine manipulation and vascular management. Augmented reality technology can project a 3D visualization liver model into the surgical area, which helps to solve the problem of uncoordinated hand and eye during surgery. The mixed reality presents the information of the virtual scene in the real environment, and establishes an information loop of interactive feedback between the real world, virtual world and user to enhance the realism of the user experience Virtual reality technology is a simulation system that can create and experience virtual worlds. It uses a virtual environment in which a computer simulates a real-world scene in three dimensions. It is a system simulation of multi-source information fusion, interactive 3D dynamic view and entity behavior that allows users to enter the environment. Virtual anatomy in virtual reality environments provides learners with a realistic 3D learning environment that allows surgeons to quickly master operational techniques and shorten training cycles.3D visualization is the first step in creating virtual reality. By blending different images, surgeons can now develop strategies based on coherent, multimodal virtual views in conjunction to surgical conditions. The application of virtual reality, augmented reality and mixed reality provides a new surgical navigation method to reduce the uncertainty between 3D reconstruction model and actual operating space. It has great potential in preoperative planning, intraoperative navigation, surgical simulation training, and doctor-patient communication Good postoperative management is also an important part of accurate liver resection. By analyzing the biological nature of the images, these imaging features are combined with clinical data to establish corresponding diagnostic and predictive models The postoperative health monitoring device can ensure to treat patients with vital signs abnormalities in a timely manner. Furthermore, wearable device can be used as effective tools to support postoperative rehabilitation training and rehabilitation evaluation The wearable device can also develop a personalized follow-up timetable once the patient is discharged from the hospital and to guide patient for follow-up after surgery. This can help the medical field to establish a complete database of clinical pathology data and collect material for the construction of big data The great advancement of biomedicine has given rise to evidence-based medicine and the return to patients' humanistic care; we see a profound change in diagnosis and treatment strategies, thinking modes and technical characteristics of traditional surgery, thus lays a new surgical paradigm. Surgery has shifted from the initial intuitive and empirical mode into the modern era. In the era of intelligent surgery, the surgeon's way of thinking will rise from image cognition and data cognition to new figurative cognition. With the continuous optimization of information computing capabilities, transmission speed and deep learning technology, the links between the various aspects of medical work will become closer and work efficiency will be significantly improved. Each medical institution is no longer an isolated closed system, but an open platform that is symbiotically born on the basis of big data sharing, enabling intelligent management of the entire data link of surgical practice. The transparency of information and neutralization of medical services will provide patients a better medical experience The digitization of surgery is a requirement for PM and it is a prerequisite for the development of surgery itself. Precision Liver Surgery is a multidisciplinary integrated system including information science and computer science. The emergence of a series of innovative intelligent diagnosis and treatment technologies has provided new strategies and means for disease diagnosis and treatment. As health is a priority, doctors are continually trying to find the ways to implement new technology and provide impactful result. The addition of digital medical technology has made hepatectomy more true to life. Digital intelligent diagnosis and treatment technology are used in the visualization of disease diagnosis, preoperative evaluation, surgical planning, real-time guidance of surgery, and training for young physicians. Deep learning gathers a massive volume of liver tumor data, including patient's laboratory data, image records, medical reports, outcomes and predicts complication. In addition, digitization allows patients to have better healthcare experience and a faster recovery.Detailed evaluation of preoperative liver function and surgical plan, individualized operation, and postoperative intervention with molecular targeting are the key factors to achieve accurate surgical treatment of liver cancer Fig. . We hope


The benefits of physical activity (PA) and social engagement for older adults are well established, yet the majority of adults over age 50 in the U.S. do not engage in regular exercise. We conducted a 4-week pre-post study with a one-month follow-up using a smart phone application called WeChat WeRun to explore the mechanisms of social engagement and competition to increase PA among older adults. Participants  were randomly assigned to one of two conditions. Participants in the activity competition condition used the WeRun to track their daily walking steps and interact via text with their group members for 4 weeks. The daily walking steps were displayed to the group and the participant with the highest daily steps in the group was indicated as the champion each day. Participants in the control group only used WeRun to track their own walking steps. Outcome variables included average weekly steps, exercise self-efficacy, and social engagement. The two-way repeated measures ANOVA results revealed that participants in the competition condition had higher average weekly steps and level of social engagement at the posttest, compared to those in the control group. Post hoc tests using the Bonferroni corrections revealed that the intervention increased their average weekly steps and social engagement from pretest to post-test, while these outcomes did not change in the control group. The effects were maintained at the one-month follow up. Discussion will consider the motivational role of competition and social interactions in increasing PA among older adults.


This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict  Arabidopsis thaliana has become a powerful model organism for studying many aspects of plant biology and adaptation to the environment  with an accuracy of approximately 3 m. The waypoints were imported into Garmin Mapsource Version 6.15.6  and projected on the OpenStreetMap2. The sampling was performed according to Arabidopsis rosettes were sampled at a developmental stage in which inflorescence and mature leaf rosettes had been established  between these accessions and a set of additional 24 accessions for which DNA sequence is publically available (see footnote 1) has been calculated by dividing the number of polymorphic sites by the number of informative sites. These values have been used to create a hierarchical clustering using the McQuitty method within the function hclust in R  under frequent cooling with liquid nitrogen for 3 min. Polar metabolites were extracted and derivatized as described previously  coupled to a LECO Pegasus® 4D GCxGC-TOF mass spectrometer . Compounds were separated on an Agilent HP5MS column . Deconvolution of the total ion chromatograms was performed using the LECO Chromatof® software. All details about injection, gradient, deconvolution, and library search parameters can be found in Frozen sample rosettes were homogenized in a ball mill  were removed from the dataset. Missing values of variables, which were missing in more than half of all measurements in a population were filled with half of the matrix minimum. The remaining missing values were imputed by random forest computation .All calculations of Jacobian matrices were performed based on a modified version of the toolbox COVAIN  within tp-values in R  were removed from the dataset. Missing values of variables, which were missing in more than half of all measurements in a population were filled with half of the matrix minimum. The remaining missing values were imputed by random forest computation  regression analysis of primary metabolites versus a response matrix comprising geographical coordinates and altitude above sea level indicated a separation of population OOE3 from populations OOE1 and OOE2 across latent variable 1 Figure . The met5 times and median values of all entries of the Jacobian matrices were determined. Principal component analysis (PCA) of the entries revealed a clear separation of the population-specific Jacobian information in which the technical variance was found to be significantly lower than the biological variance . Yet, we were able to identify 70 features that discriminated at least two of the populations .Predictions about the differentiation via signatures in secondary metabolism were validated by LC–MS metabolomics focusing on a central set of secondary metabolite backbones with close similarity to previously identified anthocyanins attached to sinapoyl moieties . Such meructures . Hence, platform . While, platform . TogetheA. thaliana (Arabidopsis in context of adaptation to macro- and micro-environmental fluctuation (In addition, results of SNP-based genotyping revealed three genetically different populations, which are, however, closer related to each other than to other European accessions Figure . In termthaliana , the genthaliana . Based octuation .in situ study for the first time and revealed a significant intraspecific biochemical plasticity within three close-by natural populations in their natural habitat. We have extracted the genes of the individual populations which distinguish them most (Supplementary Data Sheets The combination of in depth genotypic and metabolomic profiling and modeling opens up the opportunity to search for direct correlations of polymorphisms and metabolic changes. Here, we applied this concept to an Arabidopsis plants can be characterized and differentiated by their primary–secondary metabolic signature. In future studies, monitoring of micro-climatic properties will enable the characterization of sampling sites by continuous quantitative environmental data and thus improve the understanding of the ecological context of in situ molecular profiles. Additionally, biotic and abiotic habitat parameters, such as soil properties and phytosociological association, might even promote our current understanding of individual plants’ physiology. Finally, our study points to the importance of considering variance and covariance information in biological data sets (In summary, it was demonstrated that intraspecific metabolic phenotypes of geographically nearby-grown ata sets  which, tata sets .Arabidopsis thaliana, performed the measurements and statistical analysis, and wrote the manuscript. TN performed the measurements, statistical analysis, metabolic modeling, and wrote the manuscript. CG identified and collected natural populations of Arabidopsis thaliana. LF harvested sample material. AK, AP, AF, and MNo performed SNP calling, population analysis, and wrote the manuscript. WW conceived the study, performed statistical analysis, and wrote the manuscript.MNa collected natural populations of The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Almost 25% of all human cancers are located in the gastrointestinal tract (GIT), making it the dominant cancer-affected site. The reason for this could be constant GIT exposure to organ damage and chronic inflammation . DespiteThe most important risk factor for BE and EAC is gastroesophageal reflux disease (GERD). The majority of GERD patients present with heartburn and effortless regurgitation. GERD is one of the most common indications for visiting primary care physicians and gastroenterologists and the most common GIT-related diagnosis worldwide -7. The pMultifactorial pathophysiology and risk factors for GERD are still the subject of many investigations ,5-7. A rCroatian Medical Journal (Having all this in mind, we were delighted to read the article by Markoš et al, published in the current issue of the  Journal . The aut Journal .


The recognition performance of visual recognition systems is highly dependent on extracting and representing the discriminative characteristics of image data. Convolutional neural networks (CNNs) have shown unprecedented success in a variety of visual recognition tasks due to their capability to provide in-depth representations exploiting visual image features of appearance, color, and texture. This paper presents a novel system for ear recognition based on ensembles of deep CNN-based models and more specifically the Visual Geometry Group (VGG)-like network architectures for extracting discriminative deep features from ear images. We began by training different networks of increasing depth on ear images with random weight initialization. Then, we examined pretrained models as feature extractors as well as fine-tuning them on ear images. After that, we built ensembles of the best models to further improve the recognition performance. We evaluated the proposed ensembles through identification experiments using ear images acquired under controlled and uncontrolled conditions from mathematical analysis of images (AMI), AMI cropped (AMIC) (introduced here), and West Pomeranian University of Technology (WPUT) ear datasets. The experimental results indicate that our ensembles of models yield the best performance with significant improvements over the recently published results. Moreover, we provide visual explanations of the learned features by highlighting the relevant image regions utilized by the models for making decisions or predictions. Automated identity recognition based on physical characteristics of humans , such as the face, iris, fingerprints etc., has attracted a considerable amount of attention in the biometric community due to increasing security requirements. In order to be a valid biometric modality, these characteristics should be distinctive, universal, permanent, quantifiable, and convenient to measure . Out of The first large-scale study on using ear characteristics to uniquely recognize individuals was conducted by Iannarelli , in whicThe vast majority of existing ear recognition systems have been constructed by employing handcrafted feature extraction methods  to represent the ear images and a traditional classifier to match and classify the resulting feature vectors. Descriptor-based recognition techniques still offer acceptable performance for small-sized datasets acquired under controlled conditions although these descriptors can only handle specific image variations. In earlier studies, multiple ear image characteristics including texture, edges, shape contours, and gradient information were utilized to better describe the ear features. For instance, texture descriptors have been extensively studied in ear recognition, justifying the importance of texture cues when featuring ear images ,8,9. On Nowadays, deep learning , a type A first step towards building CNN-based ear recognition models is introduced in . The autAn effective solution to address ear recognition when training data is limited is to use transfer learning ,34,35. FIn 2017, Emeršič et al.  announces needed . The wins needed  which utDespite the rapid increase and wide adoption of deep learning models in various domains, their internal functionality is not fully understood and hard to interpret because of the black-box nature of deep networks . Thus, tWe propose ensembles of VGG-based models that outperform other ear recognition techniques on the considered mathematical analysis of images (AMI) and West Pomeranian University of Technology (WPUT) ear datasets. Extensive experiments using different learning methodologies were carried out and a comprehensive analysis of their performance and computational complexity is presented;We present a novel ear image set, known as AMI cropped (AMIC), based on the original AMI dataset with substantial background and profile regions removed; this is intended to be used to evaluate the performance of different ear identification and verification systems under challenging conditions;We provide visualizations of the learned features to interpret the obtained results and increase the confidence and reliability in the models’ predictions, which is useful when working with a limited amount of training data.In this paper, we choose the VGG network as one of the top performers in both image classification and object localization challenges , and intThe remainder of the paper is structured as follows. The VGG network  is a verThe original VGG network  uses RGBt (ReLU)  activatii> to refer to the different models, where i refers to the total number of weighted layers in the network. In this paper, we introduce some changes to the VGG architecture and adapt the network to better suit the ear recognition problem on the considered ear datasets. We explore four VGG-based models of increasing network depth starting from 11 up to 19 layers, although they have a design similarity for fair evaluation. We use the terminology VGG-< dropout  between The performance of recognition systems is highly dependent on the robustness and discriminative ability to represent image information. Over recent years, deep CNNs have shown a powerful capability to learn discriminative representations from images that summarize their essential factors of variations. However, learning these rich representations by deep CNN models requires a massive amount of labeled training data and powerful computational resources to train these models and optimize the large space of hyperparameters. If these requirements represent an issue for any specific application or domain, learning these representations from scratch would be unfeasible . One effOne particular advantage of deep learning methods is learning hierarchical data representations to form certain levels of abstractions. In the scope of image classification problems, lower layers process rather basic relations between pixels and basic textures, while deeper layers consider high-level information including semantic relationships. Despite the advantages and performance gain obtained by deeper network designs, increasing the network depth increases the number of trainable parameters that need to be optimized during training. The latter problem becomes more obvious in the case of dealing with smaller datasets, and more specifically, when these parameters are randomly initialized. In this paper, we use ‘training with random weight initialization’ and ‘training from scratch’ interchangeably. In order to enhance the generalization ability of the models and alleviate the overfitting problem, we adopt several regularization strategies, including dropout  and dataAnother effective strategy of using deep models to overcome the limitation of small-sized datasets is initializing the network with weights from a pretrained model instead of random weight initialization. By doing so, we benefit from the robust and discriminative filters learned by top-performing CNNs on large-scale datasets such as the ImageNet dataset . These pUsing the fixed representations learned on ImageNet dataset for general object recognition might not be the optimal choice for the specific task of ear recognition. Hence, in our third learning strategy, we adjust all layers, including the convolutional ones and further train the models using the training set of each dataset. This fine-tuning or adaptation of pretrained models helps them to see and understand the ear’s shape and structure, thus leading to better and more discriminative features and increased overall recognition performance. Indeed, it is one of the most widely used approaches of transfer learning from CNN-based models and has the advantage of accelerating the training process.Ensemble models are powerful machine learning techniques which improve the overall performance by combining the decisions from multiple models. As a result of the stochastic nature of deep neural networks where each model learns specific filters and patterns, building ensembles of deep models is an effective solution to boost the recognition accuracy. Ensembles have been adopted to compete in many recognition challenges ,44,61,62ctor see . While bIn order to analyze the performance characteristics of the suggested VGG-based network architectures in ear recognition, we conducted a series of identification experiments on three benchmark datasets. Four VGG-like network variants with increasing depth  were implemented and compared to each other using three different learning strategies. Thereafter, we selected the best performing models to build ensembles to boost the recognition performance.For the AMI and AMIC datasets, the learning rate was scheduled to have an initial value of 0.01 and to decrease depending on the learning strategy. In the case of training the networks with random weight initialization, the learning rate was divided by 2 every 100 training epochs. In the other two strategies, the learning rate was halved every 50 epochs due to their fast convergence. For the WPUT dataset, the learning rate was set initially to 0.02 and was divided by 5 every 300 epochs when training the networks from scratch and divided by 5 every 100 epochs for the other two strategies. To improve the models convergence during training, we applied batch normalization after each convolution layer. The networks were trained using back-propagation  and optiThe models were trained on a PC with Intel(R) Core(TM) i7-3770 CPU, 8 MB RAM and Nvidia GTX 1080 for 500 epochs , 200 epochs (training only fully connected layers), and 150 epochs  until convergence. However, the number of training epochs for the WPUT dataset was 900 for scratch training and 300 for the other two strategies.The following subsections give more details about the ear image datasets used and the data augmentation methods followed in this work to improve recognition accuracy and avoid overfitting of the training data by the deeper models. Furthermore, the experimental protocol and performance metrics and curves used to evaluate the models’ performance are explained.The AMI ear database  was collThe third dataset considered in our study is the WPUT ear database . Its imaRandom scaling to fill 70–100% of the canvas area;Random rotation from −45 to +45 degrees;Random horizontal shearing in the range of −5% to 5% of the image width;Random cropping, removing up to 10% of the image, keeping the aspect ratio;Resizing to fit the canvas;Gaussian blur with radius of 3 and 50% chance;Blending Gaussian noise of random amount;Random change of brightness from −20% to +20%;Random change of contrast from −40% to +40%;Random change of saturation from −20% to +20%;Random change of hue from −5% to +5% of the color range;Horizontal flipping with 50% chance;Normalization by subtracting the mean pixel and division by its standard deviation.Since the process of training deep CNNs requires a huge amount of labeled training samples to reduce overfitting, and given the fact that existing ear datasets are still limited , profound data augmentation is a crucial measure to effectively increase the number of training samples. Appearance variations are artificially introduced without any extra labeling costs by using label-preserving transformations ,67,68. WIn order to evaluate performance for all identification experiments, we plotted and computed the following performance curves and metrics: (i) Cumulative match characteristic (CMC) curves for each identification experiment to visualize the fine performance differences between the different models; (ii) rank-one (R1) and rank-five (R5) recognition rates; and (iii) area under the CMC curve (AUC).We followed the same experimental protocol for splitting each dataset into two disjointed sets: a training set, and a test set covering This section reports the results of our identification experiments using the different models and strategies on the aforementioned ear datasets. In the following subsections, we first present the obtained results under the different learning strategies using the original AMI dataset. Then, we discuss the obtained results on the AMIC dataset and assess the effect of cropping auxiliary parts from the profile images on the recognition performance. Thereafter, we evaluate the models in real-world scenarios associated with ear recognition  using the WPUT dataset. On the basis of the obtained results, we select the best performing models and explore various model combinations to build ensembles and analyze the impact on the performance metrics. Finally, with the help of Grad-CAM visualization, we present visualizations of the discriminative image regions learned by the models for inferring predictions.Our objective in this section is to measure the influence of increasing the network depth on the recognition performance when using the AMI dataset in a closed-set identification scenario. Here, we discuss the experimental results attained with our three feature learning strategies.Scratch Training: The identification experiments when training a network with randomly initialized weights suggest that the VGG-16 is the top performer with R1 of 86%, while the VGG-13 and VGG-19 models perform in a similar manner. However, for the other performance metrics of R5 and AUC, the leader is the shallower model VGG-11, indicating its competitive performance in the higher ranks. Feature Extraction: When freezing the weights of all convolutional layers and tuning only the fully connected layers of the pretrained models, we observe a worsening in the learning process compared to the above-mentioned strategy. Here, the VGG-16 network performs the best with respect to R1, exceeding Fine-Tuning Pretrained Models: Giving all layers of the pretrained models a chance to optimize their weights using the training part of the AMI dataset, we observe a noticeable improvement in recognition performance and convergence time for all models. In particular, the deeper models exhibit improvements with a large jump of above Overall, the obtained models achieve a similar performance in terms of R1 when trained from scratch, with a noticeable advantage over when the training was only for the fully connected layers of the pretrained models. It is also observed that when learning only specific parts of the pretrained models, the deeper networks do not improve the recognition performance and the best model under this learning strategy, the VGG-16 network, achieves Ensembles of Models: On the basis of the experimental results which indicate that models obtained using the fine-tuning strategy attain the highest recognition performance, we choose these models for constructing different ensembles. We have experimentally examined several combinations and checked their impact on the performance metrics. The main objective of the experiments conducted in this section was to ascertain the impact of removing auxiliary information from the profile images on training and tuning the considered deep models. The presented results in Scratch Training: When using the tightly cropped images from the AMIC dataset, training with randomly initialized weights suffers from a noticeable drop in the recognition rates compared to the AMI dataset with a margin of more than Feature Extraction: On the AMIC dataset, the VGG-11 network achieves an R1 recognition rate of above Fine-Tuning Pretrained Models: Compared to the above-mentioned strategies, tuning all parameters of a pretrained network yields a much better performance and avoids the overfitting problem reasonably well. Even though all models are influenced by the cropping process, the improvement in recognition rates is with the large margin of nearly Ensembles of Models: We also consider different combinations of models obtained on the AMIC dataset for making ensembles and measure the gain in performance metrics. We start by combining two fine-tuned models of VGG-13 and VGG-16, and we observe a slight improvement over using any of the other models separately. Surprisingly, when adding VGG-19 to the ensemble, we notice a slight improvement in the R5 and AUC metrics while getting an identical R1 recognition rate, which means that the VGG-19 model helps the ensemble to improve recognition in the higher ranks. On the other hand, adding the shallower model VGG-11 to the triplet leads to a performance gain of above In general, learning deep representations of tightly cropped ear images seems to be more challenging, especially when the cropped images introduce variable sizes and pose variations. One logical reason for the performance drop, which will be explained in more details with the help of visualizations in We also examined the model performance with respect to the network depth. For the AMIC dataset, we found that shallower models perform as well as the deeper ones as they are less prone to overfitting and have fewer parameters to optimize. Another possible interpretation for this behavior can be attributed to two things: First, on the AMI dataset, the additional information related to hair, skin, and other details increase the variance of the data and offers an incentive for learning higher level filters, which have to distinguish between haircuts, skin textures, and other details. The additional variance requires a deep network structure that is capable of learning more nonlinear and more complex filters; second, a smaller parameter space seems to be enough for this low-variance image set and should be chosen in accordance to Occam’s razor .This section reports the recognition results obtained using ear images acquired under difficult or unconstrained imaging conditions. The experiments were conducted under a similar experimental protocol. Scratch Training: When training deep models from scratch on the unconstrained WPUT dataset, a significant drop in recognition rates is observed with respect to the other datasets. A logical reason for this is the lack of sufficient training data and the difficulty introduced by the wide range of image variations. Furthermore, the WPUT dataset has approximately an equal number of images for the left and right ears, each with different viewing angles which could impel the models to learn these variations and not focus on the important features. This problem emphasizes the importance of having abundant training samples for each individual to help the models learn and generalize to the level of intraclass variations. A general observation from Feature Extraction: The performance of feature extraction through the pretrained models under uncontrolled conditions is better than training from scratch. One reason for this is the benefit from the learned filters in the low-level layers which extract more generic features. Additionally, the limited training images are used to tune only the classification block on top. Again, VGG-11 is the top performer with a R1 recognition rate of nearly Fine-Tuning Pretrained Models: The obtained results suggest that fine-tuning pretrained networks on the target task is a preferable option. As expected, the evaluation metrics are higher under this learning strategy. Despite the improvement in all metrics for all models, deeper models maximize their benefit and achieve a better performance. The models achieve R1 rates of above Ensembles of Models: We start our ensembles by combining the VGG-13 and VGG-16 as both have an identical performance of nearly Although deep learning techniques achieve state-of-the-art results, understanding and interpreting the obtained results and the reason behind network decisions are of paramount importance. In this section, we seek to better interpret what deep models have learned after training using the Grad-CAM visualization technique. We illustrate the representation learned by highlighting the regions of interest (areas where the models focus) when making a prediction decision. Even though similar conclusions are obtained for the other models, for ease of exploration, we choose the VGG-16 models obtained after training on both the AMI and AMIC datasets and visualize all the correctly classified subjects as well as the misclassified ones from both datasets, as shown in The Grad-CAM approach finds and highlights spatial image regions that strongly contribute to making a specific decision, by providing a heatmap. Thus, in In order to get an insight into the process of wrong ear identification, misclassified samples and their Grad-CAM visualizations are also added to Our results in Nevertheless, wrong decisions seem to be somewhat correlated to a stronger focus on regions close to the image boundary. It is possible that filter outputs located at the border of a spacial feature map suffer more from incomplete convolution (padding) than filter outputs corresponding to the center of a feature map. As a result, the receptive field needs more layers to actually cover some relevant part of the ear. Thus, relying on far-from-center regions might be somewhat less robust leading to a higher chance of misclassification. However, conducting the experiments with no padding actually did not further increase the performance metrics.As a result of the fact that error back-propagation requires a massive amount of numerical operations, model optimizations need to be taken into considerations as the small memory footprint and high speed of the obtained models are of paramount importance, especially if one intends to deploy the algorithm on devices with limited computational resources. As an additional step to reduce the computation time, we reduce the input image size to The aim of this work was to address the ear identification problem under constrained and unconstrained conditions using deep learning models of increasing depth and ensembles of the best models as well as to improve the recognition performance. We considered four VGG-like network architectures and three ear image representation learning strategies including: training networks with randomly initialized weights, feature extraction using pretrained networks, and fine-tuning models which were pretrained on image classification tasks. The experimental results indicate the supremacy of the fine-tuned networks on ear images compared to the other learning methods. Furthermore, the recognition performance was further improved, independent of the dataset size, when constructing ensembles of the fine-tuned networks. Despite the improved performance on all datasets with the ensembles, the improvement under uncontrolled conditions was higher due to the wide variations in images, meaning that the different models could learn more distinguishing features. Therefore, it is of a crucial importance to utilize multiple representations for ear images acquired under uncontrolled settings. Experimentally, the proposed ensembles achieved the best recognition accuracy on all datasets with a large margin of at least In order to understand what the deep models have learned and attempting to interpret the obtained results and improve ear recognition understanding, we provide visualizations of the most discriminative regions in the ear image which lead to correct or false predictions. With the Grad-CAM visualization technique, we were able to see where the models focus when making their predictions. On the basis of the results from the visualization experiments, we found that focusing on the ear shape is frequently associated with correct predictions; while considering other parts of the profile image can result in misleading predictions. On the other hand, utilizing auxiliary parts of profile images could help in distinguishing certain high-level features like gender or haircut based on visible parts of the hair or neck. Therefore, we introduced a cropped ear image set (AMIC) based on the existing profile images from the AMI dataset. The main purpose of the introduced dataset was: (i) to make the networks process only the ear shape and structure so that they can learn only the relevant ear features; (ii) to build trust in the obtained results based on exclusively seeing the ear which is the main aim of the ear recognition study; and (iii) to evaluate the performance of newly proposed ear recognition systems in the future.Even though the obtained results are promising and significantly improve the recognition performance on the considered datasets, there is still room for improvement, such as overcoming the limitations of small-scale ear datasets, and efficiently learning features that exhibit robustness to various image transformations. In future work, we plan to extend our experiments to explore various deep CNNs architectures and suitable learning strategies, whilst focusing more on the problem of unconstrained ear recognition. On the other hand, we will extend our study as regards different CNN feature visualization techniques to understand what the network or certain layers learn in order to find the most relevant regions of the human ear. A very interesting point for future study is the exploration of ways to control the attention of CNNs, allowing the networks focus only on certain discriminative regions in the input image in order to improve their learning ability.


Despite tremendous progress in improving health in the U.S. in recent years, many ethnic minority populations still experience significant health disparity gaps that stem from a lack of valid research and culturally sensitive service infrastructures for those populations. Our previous research indicates that overwhelming numbers of older Korean Americans, a first-generation immigrant group, suffer not only from health issues but also from a lack of self-confidence and a feeling of social isolation because of language and cultural barriers. In addition, many older Korean Americans lack personal resources and health literacy to overcome those barriers when they attempt to access the complicated U.S. health care system. The purpose of this symposium is to promote the understanding of the health and well-being of older Korean Americans and identify their vulnerabilities and resilience. A series of five community-based studies of older Korean Americans conducted in multiple locations  that covers diverse topics on health and well-being , using various methodologies  will be presented. The issues of diversities and disparities will be discussed from the cultural perspectives, as well as implications for future research and practice.


Snapping fingers resulting from flexor tendon tenosynovitis at the metacarpophalangeal (MCP) joint, is common. However lateral band snapping syndrome (LPSS) in the proximal interphalangeal joint (PIPJ) are extremely rare.A 43-year-old female was diagnosed with LPSS in the PIPJ of fifth finger which was confirmed by dynamic evaluation upon ultrasound. Repair of retinacular ligament of the extensor tendon was performed. At the six-month follow up, the patient had regained full ROM with no discomfort, without evidence of recurrence.The diagnosis of LPSS at the PIPJ is challenging. Dynamic evaluation of the extensor tendon over PIPJ is necessary to diagnose LBSS. Ultrasound was used to assess the movement of the lateral band of the extensor tendon when the fingers flexed and to demonstrate dynamically the snapping and subluxation of the lateral band, so as to facilitate confirming the diagnosis of LBSS.Given clinical suspicion of LBSS, we recommend ultrasonography as a feasible tool to confirm the diagnosis. Patients with LBSS may thus benefit from prompt repair of the retinacular ligament without sequela. However other causes and other locations of snapping on the fingers are extremely rare. To date, few cases of snapping fingers at the proximal interphalangeal joint (PIPJ) have been reported. The method for diagnosing snapping fingers relies more on physical examination than on specific image studies . This re2A 43-year-old female with no specific underlying disease had been bothered by symptoms of finger snapping at the PIPJ of her right small finger for more than a decade. She denied any specific episode of trauma or previous surgical history with respect to the right small finger. Upon examination, no gross abnormality over the right small finger was observed and the finger exhibited a full range of motion over the PIPJ. However, crunching of the tendon could be found over the dorsal medial side of the PIPJ during flexion of the fifth PIPJ. Ultrasonography was then utilized to evaluate the dynamic change of the extensor mechanism with a broadband 7.5–10 MHz linear array scan head with the linear transducer placed transversely and longitudinally on dorsal medial side of the fifth PIPJ.  In dynamic imaging during flexion of the right fifth PIPJ from extension, subluxation of the lateral band of the extensor tendon to the radial side causing snapping symptoms were detected under the transverse section  (Video 1Exploration of the PIPJ with a curve incision under local anesthesia revealed rupture of the retinacular ligament with partial fibrosis union, laxity and lateral subluxation of the radial side of the lateral band with partial joint capsule rupture over the dorsal PIPJ . The joiAt the six-month follow up, the patient had regained full ROM with no discomfort, without evidence of recurrence.3The PIPJ extensor mechanism involves three ligaments. The lateral band is controlled and protected by the transverse retinacular ligament and the triangular ligament. The transverse retinacular ligament prevents lateral band dorsal subluxation upon PIPJ extension. The triangular ligament prevents lateral band volar subluxation during PIPJ flexion. Since the triangular ligament and the transverse retinacular ligament are important for the function of the lateral band, the integrity and elasticity of these three ligaments are crucial to the functioning of PIP joint. As in the present case, an injury of the retinacular ligament can cause functional instability of the finger.Yamaguchi et al. identified two common causes of snapping finger based on their operative findings. One is the formation of a tumor at the flexor pollicis longus at the MCP joint tendon sheath; the other is flexor tendon tendovaginitis in the thumb, middle finger or ring finger. These causes of the snapping finger are very common. However, a snapping finger at the PIP joint that arises from an injury of the extensor mechanism is extremely rare and only three cases have been reported in English ,3,4.One case involved snapping of the left small finger at the PIPJ as a result of post-traumatic retinacular ligament injury in a 24 year-old man . AnotherThe diagnostic tools in the three cases were the plain film ,3 and thAlthough snapping at the PIPJ is a specific symptom of LBSS, the other potential differential diagnosis may also include lateral band rupture of extensor tendon or ganglion cyst of tendon sheath. However, all of the tentative differential diagnosis can be excluded using ultrasonography . Ultraso4The case reported here demonstrates that when snapping or instability of the PIPJ of the fingers is present, LPSS, which may be benefit from surgical repair, should be listed as a differential diagnosis. Ultrasonography can be a reliable tool for confirming the anatomical and dynamic functional instability of finger extensor mechanisms.No funding for our research.The ethical approval was registered as TMU-JIRB N201810021.Informed consent for publication, including any necessary pho-tographs, has been obtained from the patient.Tyng-Shiuan Hsieh: First author, writing.Yi-Jie Kuo: data collections.Chen Yu Pin: Corresponding author.Our manuscript is a case report not a research.Yu-Pin Chen.Not commissioned, externally peer-reviewed.All authors have no conflict of interest.


The effectiveness of combined antiretroviral therapy, not only allows people living with HIV (PLHIV) to live longer, but also exposes them to extra cardiometabolic risks. The recent report of the International AIDS (Acquired Immune Deficiency Syndrome) Society and the Lancet commission are clear on the fact that, the world is not on track regarding ending the HIV/AIDS pandemic by the year 2030 (UNAIDS 90-90-90 targets). We highlight the priority public health interventions, as well as research actions to cope with this almost inevitable triple burden of disease for the continent. Improvement of routine data collection within the health systems strengthening agenda, prioritization of primary health care as the cornerstone of an effective health care system to contain HIV as a chronic disease, and multiplication of large cohort studies in Sub-Saharan Africa are the main public health and research imperatives. With the advent of combined anti-retroviral therapy, people living with HIV live longer and have their quality of life has improved dramatically. The evidence of increased risk of cardiovascular disease (CVD) among these persons is almost conclusive , 2. TriaThe Ndlovu Cohort Study, a prospective study from the Moutse area, Limpopo Province, in South Africa, which shall recruit 1000 HIV-positive and 1000 HIV-negative participants aged 18 years and older . The aimThe EndoAfrica study shall provide a unique opportunity to recruit a cohort of HIV-infected patients and HIV-negative controls, who will be comprehensively and longitudinally assessed for cardiovascular risk and disease profile, with vascular endothelial function as a potentially important intermediate cardiovascular phenotype . The resThere are about 37 million persons living with HIV worldwide . In 2016It is idle to think that these prospective studies shall be enough to provide sufficient data, to monitor and evaluate the HIV pandemic in Africa. Initiatives that promote routine quality data collection within the health systems strengthening agenda, especially in Africa, will not only benefit effective research and policy recommendations in PLHIV, but also the entire health needs spectrum. The global health community is not on track as far as ending the HIV pandemic by 2030 (UNAIDS 90-90-90 targets) . With HILB conceived the initial idea, searched the literature and wrote the initial version of the manuscript. GG provided intellectual input. All authors have read and agreed on the final version of the submitted paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cephalorhynchus hectori) and little penguins (Eudyptula minor). The relative abundance of the two predator species during surveys was modelled according to the relative abundance of potential prey using generalised additive mixed models. Schools of epipelagic fish were readily detected by the RGE system and were more abundant in summer compared to winter. The models performed well, explaining 43% and 37% of the deviance in relative abundances of dolphins and penguins respectively. This is the first study to link the distribution of Hector’s dolphin to that of their epipelagic prey and confirms the utility of RGE in studies of habitat use in marine predators. Limitations associated with a lack of formal acoustic calibration and data formatting can be overcome and would make RGE valuable, inexpensive tools for investigating variability in populations of small pelagic fishes.Quantifying the distribution of prey greatly improves models of habitat use by marine predators and can assist in determining threats to both predators and prey. Small epipelagic fishes are important prey for many predators yet their distribution is difficult to quantify due to extreme patchiness. This study explores the use of recreational grade echosounders (RGE) to quantify school characteristics of epipelagic fish and link their distribution to that of their predators at Banks Peninsula, New Zealand. The hydro-acoustic system was ground-truthed with 259 schools of epipelagic fish. During 2015 and 2016, 136 hydro-acoustic surveys were conducted with concurrent observations of Hector’s dolphin ( The distribution of marine top predators generally reflects that of their prey –3. For tPatchiness over multiple temporal and spatial scales ,5,13, isFor many research programmes the significant cost involved with the purchase or hire of a scientific echo-sounder (SES), and the expertise or logistic requirements to operate such equipment, present major hurdles. These factors may compromise the repeatability of surveys and therefore constrain the sample size required to resolve a patchy prey field. Several modern recreational grade echo-sounders (RGE) allow on-board recording of the digital acoustic data, and could, within certain limitations, provide an alternative to SES. RGEs have been used to quantify aspects of the prey community in deep water habitats  and coasLittle is known about the distribution of epipelagic schooling fish in New Zealand waters, particularly at the fine scales relevant to determine overlap with marine predators, see ,24–26 foCephalorhynchus hectori) is an endangered coastal dolphin that is found in shallow (<100m) waters around the South Island of New Zealand. Banks Peninsula is a stronghold for this endemic species. The dolphins have a generalist diet focussing on species throughout the water column, but epipelagic prey  contribute significantly to their diet )A final limitation is the small volume of water sampled for prey due to the narrowness of the beam in shallow water habitat. This is not a limitation of RGE per se, with SES facing similar challenges in shallow water ,71. WhenDespite these limitations, there was clearly a strong overlap between predators and potential prey in this study. This provides evidence that RGE can be used as a tool to investigate habitat-use in coastal predators. Distribution models for both predator species explained a good proportion of deviance in the response variable; comparable and in some cases exceeding those from other studies linking predator and prey distribution ,7,66,69.This is the first study to establish overlap between Hector’s dolphins and little penguins with their epipelagic prey. These predators have an endangered and threatened status, respectively ,74. SubsInternationally, there has been substantial concern about the state of stocks of small epipelagic fishes ,29,75. TS1 FigThe depth dependence of a mean backscatter values from selection of schools detected during predator-prey surveys is shown before (a) and after (b) correction.(TIF)Click here for additional data file.S1 FileThe file contains datasets for the ground-truthing exercise and the predator-prey surveys.(ZIP)Click here for additional data file.


To compare magnetic resonance imaging and intraoperative findings in patients diagnosed with traumatic injury to the brachial plexus. Patients with a diagnosis of traumatic injury to the brachial plexus admitted to the hand and microsurgery outpatient consult of the Hospital das Clínicas at the University of São Paulo were selected during December 2016. A total of three adult patients with up to six months of injury who underwent surgical treatment were included in the study. A diffusion-weighted sequence magnetic resonance protocol and fluid-sensitive volumetric reformatting sequence were applied. The magnetic resonance results were compared with the diagnoses obtained from the injuries observed during the surgery. The study was double-blind (surgeon and radiologist). A descriptive correlation was found between the magnetic resonance imaging results and the diagnostic findings from the surgeries, for both pre- and post-ganglionic injuries. Magnetic resonance imaging has shown to be a promising diagnostic method in preoperative assessment of brachial plexus lesions; it is less invasive than other common methods, showing not only avulsion lesions but also localized postganglionic lesions in the supra- and infraclavicular region. Level of Evidence III; Diagnostic studies - Investigating a diagnostic test. For decision-making and prognosis, they are commonly classified into three categories according to anatomical parameters: pre-ganglionic (root avulsion), post-ganglionic (rupture or injury in the continuity of the nerve), or a combination of both.-,In this context, electrodiagnostic and imaging tests have become important tools in anatomical location of these injuries. Currently, the most commonly used imaging methods are myelography, computerized myelotomography, and magnetic resonance imaging (MRI).-Specifically in relation to the brachial plexus, MRI should be performed according to a specific protocol to obtain slices and images that can be used to stage the injuries. One differential sequencing technique is diffusion-weighted MRI of the brachial plexus, also called diffusion-weighted neurography. This sequence permits greater contrast between the nerve structures and surrounding tissue, enhancing the image of long stretches of the brachial plexus nerves, revealing both the morphological characteristics of the structures involved  and the pathological characteristics of the nerves . When several planes are combined, the high contrast of diffusion-weighted neurography can provide a three-dimensional evaluation, permitting better location of the lesions.The objective of this study is to conduct a comparative assessment between preoperative diagnosis using MRI with a specific technique for the brachial plexus and intraoperative findings.The study protocol was approved in advance by the institutional review board (process 10612/13).Patients included in the study were adults (above 18 years) with clinical diagnosis of brachial plexus injury with traumatic etiology who were admitted to the outpatient hand and microsurgery clinic at the Institute of Orthopedics and Traumatology in the Hospital das Clínicas de São Paulo during December 2016, with up to six months of injury and indication for surgical treatment. The exclusion criteria were patients requiring sedation or with contraindications to MRI.Three patients were included, all men with an average age of 30.66 years, all victims of motorcycle accidents. All patients signed an informed consent form.In addition to the preoperative clinical evaluation, the patients underwent MRI to obtain brachial plexus imagery using a specific diffusion-weighted sequencing protocol and sequencing with fluid-sensitive volumetric reformatting. The examinations were performed using a GE HDXT 1.5-Tesla device , 12.0 version software, and 33mT/min gradients.The surgeries were performed by the same team, and the dissection stages followed a standard sequence. Dissection was performed using the supra-clavicular transverse approach (two centimeters above the clavicle), where the roots and trunks are easily accessed. After making the incision in the skin and subcutaneous tissue, the omohyoid muscle was dissected and sectioned, providing an important anatomical landmark of the topography of the proximal brachial plexus and providing access to the plane of the roots and trunks. Generally, the first element of the brachial plexus that is seen is the upper trunk. The C5 root is smaller, vertical, above and to the side of the C6 root. The C7 root is below the transverse cervical artery, more horizontal than C6. The C8 root is located just above and behind the subclavian artery, which lies directly above the T1 root. The intraoperative evaluation was meant to diagnose and classify the lesions as avulsion (solution of complete continuity of a pre-ganglionic root), rupture (solution of complete continuity of a post-ganglionic segment), or lesion in continuity .Intraoperative decisions related to the treatment of brachial plexus lesions were not influenced by the study.The study was conducted in a double-blind manner: the surgeons did not have access to the preoperative MRI imaging, and the radiologists had no access to the surgical findings.A brachial plexus inventory was created and completed by the surgeon and the radiologist. These data were compared to establish the degree of correlation between them.A close descriptive correlation was seen between the MRI reports using the technique in question and the diagnostic findings from the surgeries of the three patients.intraoperative findings: C5 and C6 roots and neuroma were identified in the continuity of the upper trunk. Description of MRI: Thickening and increased signal in the C5 and C6 roots extending to the upper trunk, compatible with post-ganglionic lesion in continuity. No signs of pre-ganglionic lesions. intraoperative findings: Neuroma identified in the continuity of the upper trunk. Description of MRI: Thickening and increased signal in the C5 and C6 roots extending to the upper trunk, compatible with post-ganglionic lesion in continuity. Intraoperative findings: Post-ganglionic neuroma-type C5 lesion in continuity; avulsion of the C6 and C7 roots; post-ganglionic lesion at the level of the division of the lower trunk, neuroma-type in continuity. Description of MRI: Thickening and increased signal in the C5 root and region of the division of the lower trunk. Formation of pseudo-meningocele in the emergence of the C6 and C7 roots, suggestive of avulsion (pre-ganglionic lesion). The incidence of traumatic brachial plexus injuries is increasing in Brazil, mainly due to the increase in motorcycle accidents.The earlier surgical reconstruction of the brachial plexus is performed (when this is indicated), the better the prognosis. In patients with symptoms suggesting favorable neuropraxis or partial lesions, watchful treatment may be an option if additional high-accuracy tests are not available to stage the lesions. In these cases, it is possible to wait for clinical improvement over time, avoiding an unnecessary surgical procedure. But in lesions with unfavorable prognoses (axonotmesis and neurotmesis), time is a decisive factor in surgical reconstruction. In these cases, earlier staging of lesions and indication for surgery provides better reconstructive planning and prognosis.Topographic knowledge of the lesions prior to surgery is important to avoid unnecessary dissections. Consequently, if the type and location of the lesions have not been precisely determined before surgery, supra- and infraclavicular approaches may be performed for this purpose, increasing morbidity and surgical time.Currently, the most commonly used imaging exams are myelography, computerized myelotomography, and MRI using the conventional technique. The first two are more accurate in diagnosing root avulsion, but do not offer good definition of the brachial plexus as a whole. Furthermore, these tests present higher morbidity because of the use of intrathecal contrast. Conventional MRI, in turn, does not provides images with the definition needed for correct staging of the lesions.Therefore, when MRI with diffusion-weighted sequences and volumetric reformatting of fluid-sensitive sequences is applied to traumatic lesions of the brachial plexus it provides new diagnostic perspectives, with sensitivity and specificity similar to those of computerized myelotomography for diagnosing pre-ganglionic lesions (avulsion) and greater accuracy for post-ganglionic lesions.In this study, we used a macroscopic classification of neural injury to compare with the MRI findings. Among the classifications, lesions in continuity  may not have a good functional correlation and may exhibit varying degrees of axonal involvement ranging from neuropraxis to neurotmesis.This is a preliminary study carried out over a short period of time, which explains the low number of patients evaluated. Studies with larger numbers of patients should be performed in order to confirm these data.The study showed that the MRI technique described has good accuracy in diagnosing lesions of the brachial plexus throughout its entire extension, allied with lower morbidity.The specific MRI technique described herein is a promising diagnostic method for brachial plexus lesions, and provides good accuracy in identifying the type of lesion and topography. Unlike other imaging methods, it not only permits visualization of whether avulsion is present, but also studies the entire length of the brachial plexus. It can consequently help the medical team select treatment , make decisions about the most appropriate time to operate, and select the surgical technique, reducing operative time and thus allowing better prognosis for these patients.


The Editorial on the Research TopicA Revised Approach to the Pathogenesis and Diagnosis of Myelodysplastic Syndromes With Therapeutic ImplicationsIn 1938, Cornelius Packard Rhoads and his colleague William Halsey Barker from the Hospital of the Rockefeller Institute for Medical Research in New York described the first cases of what they termed “refractory anemia” in patients with acquired anemia and refractory to treatment with iron and liver extract. Since then the most common method of investigation has been the cytological evaluation of the bone marrow (BM) aspirates [reviewed in Ref. .However, recent years have seen advances in MDS diagnosis. Emerging technologies in molecular biology, such as next-generation sequencing, have enabled identification of mutations associated with MDS  and of tNevertheless, many questions remain open in this field. For example, how can the detection of an MDS-associated mutation help establish diagnosis in cases with no obvious clinical presentation and morphology? How can MFC help in the detection of clonal hematopoiesis or maturation abnormalities? How does the BM microenvironment contribute to the initiation and progression of the disease?This research topic aims to bring to the attention of readers the recently proposed hypotheses for the underlying mechanisms in MDS and to highlight the utility of MFC for MDS diagnosis. It consists of five articles, including two original research articles and three reviews, elaborated by 18 authors holding different views on MDS pathogenesis and diagnosis.Sallman et al. provide a comprehensive overview of the role of inflammation in MDS pathogenesis. The alarmin S100A8/9-primed microenvironment mediates the HSPC death by pyroptosis and is responsible for the propagation of the MDS clone through the assembly and activation of the redox-sensitive NLRP3 inflammasome and β-catenin, as well as by promoting mutations in HSPC founder genes.Lambert et al. highlight the role of innate and specific immunity in BM immune surveillance and protection against MDS clone evolution and progression toward secondary leukemia. The pathological HSPC clones can take over the immune system by producing immuno-modulatory cytokines  and growth factors  and by modulating Treg cell activity. In addition, the immune system itself can suffer from MDS progression due to central cytopenia installment, Treg accumulation, exhaustion of phagocytosis and cytotoxicity, repertoire skewing, and possible autoimmune disorders .Wu et al. provide evidence that the stromal microenvironment is fundamentally altered in low-risk MDS (LR-MDS). The mesenchymal stromal cells (MSCs) in LR-MDS are characterized by a remarkable deficit in focal adhesion kinase that is downregulated and hypo-activated in this setting. In addition, MSCs in LR-MDS show impaired growth and clonogenic capacity and have a higher propensity to pro-adipogenic differentiation and attenuated osteogenic capacity. Along with reduced expression of SDF-1, these MSC abnormalities might be responsible for creating an unfavorable microenvironment for hematopoiesis.Aanei et al. summarize the advantages and limitations of different approaches currently used in flow cytometry for MDS diagnosis. The authors stress the need to include criteria for BM hemodilution evaluation in order to avoid misinterpretation of antigen expression patterns or of maturation profiles. In addition, the rating of the contribution of each phenotypic marker used in MDS panels and identification of those which are non-qualifying might improve the various screening scores currently used for MDS diagnosis.Picot et al. show that the monocyte subset evaluation by MFC, due to its simplicity and robustness, provides relevant information for CMML diagnosis. The authors suggest a ranking of biological tests for CMML diagnosis, first relying on the MFC test of blood samples that may exclude non-CMML patients from further invasive procedures. The NGS test and complete evaluation, including BM exploration, should be reserved for patients with abnormal MFC results.CA, LC, and DG are co-editors of this research topic.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.