Heart rate variability (HRV), the change in the time intervals between adjacent heartbeats, is an emergent property of interdependent regulatory systems that operate on different time scales to adapt to challenges and achieve optimal performance. This article briefly reviews neural regulation of the heart, and its basic anatomy, the cardiac cycle, and the sinoatrial and atrioventricular pacemakers. The cardiovascular regulation center in the medulla integrates sensory information and input from higher brain centers, and afferent cardiovascular system inputs to adjust heart rate and blood pressure via sympathetic and parasympathetic efferent pathways. This article reviews sympathetic and parasympathetic influences on the heart, and examines the interpretation of HRV and the association between reduced HRV, risk of disease and mortality, and the loss of regulatory capacity. This article also discusses the intrinsic cardiac nervous system and the heart-brain connection, through which afferent information can influence activity in the subcortical and frontocortical areas, and motor cortex. It also considers new perspectives on the putative underlying physiological mechanisms and properties of the ultra-low-frequency (ULF), very-low-frequency (VLF), low-frequency (LF), and high-frequency (HF) bands. Additionally, it reviews the most common time and frequency domain measurements as well as standardized data collection protocols. In its final section, this article integrates Porges' polyvagal theory, Thayer and colleagues' neurovisceral integration model, Lehrer et al.'s resonance frequency model, and the Institute of HeartMath's coherence model. The authors conclude that a coherent heart is not a metronome because its rhythms are characterized by both complexity and stability over longer time scales. Future research should expand understanding of how the heart and its intrinsic nervous system influence the brain. The heart is about the size of a closed fist, weighs between 250 and 350 g, and beats approximately 100,000 times a day and 2.5 billion times during an average lifetime. The muscular heart consists of two atria and two ventricles. The atria are upper receiving chambers for returning venous blood. The ventricles comprise most of the heart's volume, lie below the atria, and pump blood from the heart into the lungs and arteries. Deoxygenated blood enters the right atrium, flows into the right ventricle, and is pumped to the lungs via the pulmonary arteries, where wastes are removed and oxygen is replaced. Oxygenated blood is transported through the pulmonary veins to the left atrium and enters the left ventricle. When the left ventricle contracts, blood is ejected through the aorta to the arterial system  and diastole (ventricular relaxation). During systole, blood pressure (BP) peaks as contraction by the left ventricle ejects blood from the heart. Systolic BP is measured during this phase. During diastole, BP is lowest when the left ventricle relaxes. Diastolic BP is measured at this time.The heart contains autorhythmic cells that spontaneously generate the pacemaker potentials that initiate cardiac contractions. These cells continue to initiate heartbeats after surgeons sever all efferent cardiac nerves and remove a heart from the chest cavity for transplantation. Autorhythmic cells function as pacemakers and provide a conduction pathway for pacemaker potentials.The sinoatrial (SA) node and atrioventricular (AV) node are the two internal pacemakers that are primarily responsible for initiating the heartbeat. The electrocardiogram (ECG) records the action of this electrical conduction system and contraction of the myocardium Figure .In a healthy heart, the SA node initiates each cardiac cycle through spontaneous depolarization of its autorhythmic fibers. The SA node's intrinsic firing rate of about 60–100 action potentials per minute usually prevents slower parts of the conduction system and myocardium (heart muscle) from generating competing potentials. The AV node can replace an injured or diseased SA node as pacemaker and spontaneously depolarizes 40–60 times per minute. The SA node generates an electrical impulse that travels through the atria to the AV node in about 0.03 s and causes the AV node to fire Figure . The P wThe signal rapidly spreads through the AV bundle reaching the top of the septum. These fibers descend down both sides of the septum as the right and left bundle branches and conduct the action potential over the ventricles about 0.2 s after the appearance of the P wave. Conduction myofibers, which extend from the bundle branches into the myocardium, depolarize contractile fibers in the ventricles (lower chambers), resulting in the QRS complex followed by the S-T segment. Ventricular contraction (ventricular systole) occurs after the onset of the QRS complex and extends into the S-T segment. The repolarization of ventricular myocardium generates the T wave about 0.4 s following the P wave. The ventricles relax (ventricular diastole) 0.6 s after the P wave begins  and parasympathetic nervous system (PNS). PNS activity predominates at rest, resulting in an average HR of 75 beats per minute (bpm). This is significantly slower than the SA node's intrinsic rate, which decreases with age from an average 107 bpm at 20 years to 90 bpm at 50 years Opthof, . The parA major cardiovascular center, located in the medulla of the brainstem, integrates sensory information from proprioceptors (limb position), chemoreceptors (blood chemistry), and mechanoreceptors  from the heart and information from the cerebral cortex and limbic system. The cardiovascular center responds to sensory and higher brain center input by adjusting heart rate via shifts in the relative balance between sympathetic and parasympathetic outflow  nerves, which slow HR, and the sympathetic nerves, which accelerate it. At rest, both sympathetic and parasympathetic nerves are tonically active with the vagal effects dominant. Therefore, HR reflects the relative activity of the sympathetic and parasympathetic systems; with the more important question being, is the relative balance (HR) appropriate for the context the person is engaged in at any given moment? In other words, is HR higher during the daytime and when dealing with challenging tasks, and lower at night, during sleep or when not engaged in challenging duties or activities?The most obvious effect of vagal activity is to slow or even stop the heart. The vagus nerves are the primary nerves for the parasympathetic system and innervate the intrinsic cardiac nervous system and project to the SA node, AV node, and atrial cardiac muscle. Increased efferent activity in these nerves triggers acetylcholine release and binding to muscarinic (mainly M2) receptors. This decreases the rate of spontaneous depolarization in the SA and AV nodes, slowing HR. Because there is sparse vagal innervation of the ventricles, vagal activity minimally affects ventricular contractility.The response time of the sinus node is very short and the effect of a single efferent vagal impulse depends on the phase of the cardiac cycle at which it is received. Thus, vagal stimulation results in an immediate response that typically occurs within the cardiac cycle in which it occurs and affects only one or two heartbeats after its onset. After cessation of vagal stimulation, HR rapidly returns to its previous level. An increase in HR can also be achieved by reduced vagal activity or vagal block. Thus, sudden changes in HR (up or down) between one beat and the next are parasympathetically mediated  are afferents, and signals are sent to the brain via cardiovascular afferents to a greater extent than by any other major organ Cameron, . MechaniShort-term regulation of BP is accomplished by a complex network of pressure-sensitive baroreceptors or mechanosensitive neurons which are located throughout the heart and in the aortic arch. Since BP regulation is a central role of the cardiovascular system, the factors that alter BP also affect fluctuations in HR. Intrinsic cardiac afferent sensory neurons Figures , 5 transThe heart not only functions as an intricate information processing and encoding center  can be used to identify the specific pathways and influence of afferent input from the heart to the brain. HEPs are segments of electroencephalogram (EEG) that are synchronized to the heartbeat. The ECG R-wave is used as a timing source for signal averaging, resulting in waveforms known as HEPs. Changes in these evoked potentials associated with the heart's afferent neurological input to the brain are detectable between 50 and 550 ms after each heartbeat. There is a replicable and complex distribution of HEPs across the scalp. Researchers can use the location and timing of the various components of HEP waveforms, as well as changes in their amplitudes and morphology, to track the flow and timing of cardiovascular afferent information throughout the brain  and ultra-low-frequency (ULF)] that extend below 0.04 Hz. Due to their long time periods, researchers use 24-h HRV recordings to provide comprehensive assessment of their fluctuations  sensors to detect the interbeat interval (IBI). While the ECG method had been considered to be more accurate than the PPG method because early software algorithms could more easily detect the sharp upward spike of the R wave than the curved peak of the blood volume pulse signal, newer algorithms have improved peak detection from the pulse wave. The ECG method should be used when recordings are contaminated by frequent abnormal beats , since the ECG's morphology and timing properties allow software algorithms to discriminate normal sinus beats from ectopic beats , the IBI values between ECG and PPG are highly correlated . In this sample, a measure of cardiac autonomic balance did not predict previous MIs  analysis and time domain analysis. In both methods, the time intervals between each successive normal QRS complex are first determined. All abnormal beats not generated by sinus node depolarizations are eliminated from the record.Analogous to the EEG, we can use power spectral analysis to separate HRV into its component rhythms that operate within different frequency ranges Figure . PSD anaFigure The Task Force report (1996) divided heart rhythm oscillations into four primary frequency bands. These included the HF, LF, VLF, and ULF bands. The Task Force report also stated that the analysis should be done on 5-min segments, although other recording periods are often used. When other recording lengths are analyzed and reported, the length of the recording should be reported since this has large effects on both HRV frequency and time domain values.The HF spectrum is the power in each of the 288 5-min segments (monitored during a 24-h period) in the range from 0.15 to 0.4 Hz. This band reflects parasympathetic or vagal activity and is frequently called the respiratory band because it corresponds to the HR variations related to the respiratory cycle. These HR changes are known as respiratory sinus arrhythmia (RSA). Heart rate accelerates during inspiration and slows during expiration. During inhalation, the cardiovascular center inhibits vagal outflow resulting in speeding the heart rate. Conversely, during exhalation, it restores vagal outflow resulting in slowing the heart rate via the release of acetylcholine  activity has been found in a number of cardiac pathologies and in patients under stress or suffering from panic, anxiety, or worry. Lowered parasympathetic activity may primarily account for reduced HRV in aging  more frequently. The greater their stretch or detection of an increased rate of change, the more frequently baroreceptors fire action potentials. Baroreceptor action potentials are relayed to the NST in the medulla, which uses baroreceptor firing frequency to measure BP. Increased activation of the NST inhibits the vasomotor center and stimulates the vagal nuclei. The end-result of baroreceptor activations tuned to pressure increases is inhibition of the SNS and activation of the PNS. By coupling sympathetic inhibition with parasympathetic activation, the baroreflex maximizes BP reduction when BP is detected as too high. Sympathetic inhibition reduces peripheral resistance, while parasympathetic activation depresses HR (reflex bradycardia) and contractility. In a similar manner, sympathetic activation, along with inhibition of vagal outflow, allows the baroreflex to elevate BP. Baroreflex gain is commonly calculated as the beat-to-beat change in HR per unit of change in BP. Decreased baroreflex gain is related to impaired regulatory capacity and aging.The existence of a cardiovascular system resonance frequency, which is caused by the delay in the feedback loops in the baroreflex system, has been long established . During periods of slow respiration rates, vagal activity can easily generate oscillations in the heart rhythms that cross over into the LF band , the controversy is resolved. Recall that the power in the LF band can be influenced by vagal, sympathetic, and baroreflex mechanisms depending on the context, whereas HF power is produced by the efferent vagal activity due to respiratory activity. It is often assumed that a low LF/HF ratio reflects greater parasympathetic activity relative to sympathetic activity due to energy conservation and engaging in “tend-and-befriend” behaviors Taylor, . HoweverThe VLF band is the power in the HRV power spectrum range between 0.0033 and 0.04 Hz. Although all 24-h clinical measures of HRV reflecting low HRV are linked with increased risk of adverse outcomes, the VLF band has stronger associations with all-cause mortality than the LF and HF bands . Oscillations or events in the heart rhythm with a period of 5 min or greater are reflected in this band and it can only be assessed with 24-h and longer recordings  stated that analysis of 24-h recordings should divide the record into 5-min segments and that HRV analysis should be performed on the individual segments prior to the calculation of mean values. This effectively filters out any oscillations with periods longer than 5 min. However, as shown in Figure Time domain measures are the simplest to calculate and include the mean normal-to-normal (NN) intervals during the entire recording and other statistical measures such as the standard deviation between NN intervals (SDNN). However, time domain measures do not provide a means to adequately quantify autonomic dynamics or determine the rhythmic or oscillatory activity generated by the different physiological control systems. Since they are always calculated the same way, data collected by different researchers are comparable, but only if the recording lengths are exactly the same and the data are collected under the same conditions.Time domain indices quantify the amount of variance in the IBI using statistical measures. For 24-h recordings, the three most important time domain measures are the SDNN, the SDNN index, and the RMSSD. For short-term assessments, the SDNN, RMSSD, pNN50, and HR Max – HR Min are most commonly reported.The SDNN is the standard deviation of the normal (NN) sinus-initiated IBI measured in milliseconds. This measure reflects the ebb and flow of all the factors that contribute to heart rate variability (HRV). In 24-h recordings, the SDNN is highly correlated with ULF and total power  for each of the 5-min segments during a 24-h recording. Like the SDNN, it is measured and reported in milliseconds. This index is correlated with the SDNN and is generally considered redundant.The SDNN index is the mean of the standard deviations of all the NN intervals for each 5-min segment of a 24-h HRV recording. Therefore, this measurement only estimates variability due to the factors affecting HRV within a 5-min period. It is calculated by first dividing the 24-h record into 288 5-min segments and then calculating the standard deviation of all NN intervals contained within each segment. The SDNN Index is the average of these 288 values. The SDNN index is believed to primarily measure autonomic influence on HRV. This measure tends to correlate with VLF power over a 24-h period.The RMSSD is the root mean square of successive differences between normal heartbeats. This value is obtained by first calculating each successive time difference between heartbeats in milliseconds. Then, each of the values is squared and the result is averaged before the square root of the total is obtained. The RMSSD reflects the beat-to-beat variance in heart rate and is the primary time domain measure used to estimate the vagally-mediated changes reflected in HRV. While the RMSSD is correlated with HF power  and most researchers prefer it to the pNN50  slows the heart rate, yet has an opposite effect in the lungs as it increases bronchial tone. According to Porges'  polyvagaIn Porges' view, the evolution of the ANS was central to the development of emotional experience and affective processes central to social behavior. As human beings, we are not limited to fight, flight, or freezing behavioral responses. We can self-regulate and initiate pro-social behaviors  when we encounter stressors. Porges calls this the social engagement system and the theory suggests that this system depends upon the healthy functioning of the myelinated vagus, a vagal brake, which allows for self-regulation and ability to calm ourselves and inhibit sympathetic outflow to the heart. This implies that standardized assessment of vagal tone could serve as a potential marker for one's ability to self-regulate.The theory suggests that the evolution and healthy function of the ANS sets the limits or boundaries for the range of one's emotional expression, quality of communication, and ability to self-regulate emotions and behaviors. The theory describes the details of the anatomical connections from higher brain structures with the centers involved in autonomic regulation and argues that the afferent systems are an important aspect of the ANS. The theory provides insights into the adaptive nature of physiological states and suggests these states support different types or classes of behavior Porges, .The SNS, in concert with the endocrine system, responds to threats to our safety through mobilization, fight-or-flight, and active avoidance. The SNS responds more slowly and for a longer period of time  than the vagus system. According to this theory, quality communication and pro-social behaviors can only be effectively engaged when these defensive circuits are inhibited.Thayer and Lane  outline Thayer et al.  contend The CAN model predicts reduced HRV and vagal activity in anxiety. Friedman  argues tMcCraty and Childre  at the IThey use the term “physiological coherence” to describe the orderly and stable rhythms generated by living systems. Physiological coherence is used broadly and includes all of the specific approaches for quantifying the various types of coherence measures, such as cross-coherence , or synchronization among systems , auto-coherence (stability of a single waveform such as respiration or HRV patterns), and system resonance.“A coherent heart rhythm is defined as a relatively harmonic (sine-wave-like) signal with a very narrow, high-amplitude peak in the LF region of the HRV power spectrum with no major peaks in the VLF or HF regions. Coherence is assessed by identifying the maximum peak in the 0.04–0.26 Hz range of the HRV power spectrum, calculating the integral in a window 0.030 Hz wide, centered on the highest peak in that region, and then calculating the total power of the entire spectrum. The coherence ratio is formulated as: ” (14).As discussed above, neurocardiology research has established that heart-brain interactions are remarkably complex. Patterns of baroreceptor afferent activity modulate CNS activity over time periods that range from milliseconds to minutes; that is, not only within a cardiac cycle  typically increase their firing rates when the rate of change in the function to which they are tuned increases. Heart rhythm coherence, which is characterized by increased beat-to-beat variability and the rate of heart rate change, increases vagal afferent traffic from the cardiovascular system to the brain. This perspective is supported by the MacKinnon et al.  HEP studThere has been increasing interest in treating a wide range of disorders with implanted pacemaker-like devices for stimulating the vagal afferent pathways. The FDA has approved these devices for the treatment of epilepsy and depression, and they have been investigated in treating obesity, anxiety, and Alzheimer's disease  at that frequency and moves the peak frequency from the HF range (≈0.2 Hz) to the LF range (≈0.1 Hz). Breathing at the resonance frequency more than doubles the energy in the LF band (0.04–0.15 Hz). This corresponds to the Institute of HeartMath's heart rhythm coherence, which is associated with a “narrow, high-amplitude, easily visualized peak” from 0.09 to 0.14 Hz  heart rhythms with a single peak in the LF region and no significant peaks in the VLF and HF regions  and no significant peaks in the VLF and HF regions. Emotional self-regulation strategies can contribute to improved client health and performance, alone, or in combination with HRVB training. A coherent heart is not a metronome since its rhythms are characterized by dynamic complexity with stability over longer time scales.Neither Dr. Fred Shaffer nor Mr. Christopher L. Zerr have any relevant affiliation or financial involvement with any organization or entity with a financial interest or financial conflict with the subject matter discussed in the manuscript. Dr. Rollin McCraty is the Chief Scientist for the Institute of HeartMath, which has generously contributed several of the graphics used in this manuscript.


The liquid junction potential (LJP), the phenomenon that occurs when two electrolyte solutions of different composition come into contact, prevents accurate measurements in potentiometry. The effect of the LJP is usually remarkable in measurements of diluted solutions with low buffering capacities or low ion concentrations. Our group has constructed a simple method to eliminate the LJP by exerting spatiotemporal control of a liquid junction (LJ) formed between two solutions, a sample solution and a baseline solution (BLS), in a flow-through-type differential pH sensor probe. The method was contrived based on microfluidics. The sensor probe is a differential measurement system composed of two ion-sensitive field-effect transistors (ISFETs) and one Ag/AgCl electrode. With our new method, the border region of the sample solution and BLS is vibrated in order to mix solutions and suppress the overshoot after the sample solution is suctioned into the sensor probe. Compared to the conventional method without vibration, our method shortened the settling time from over two min to 15 s and reduced the measurement error by 86% to within 0.060 pH. This new method will be useful for improving the response characteristics and decreasing the measurement error of many apparatuses that use LJs. Solutions are often described in terms of their pH, a common index of acidity or alkalinity used in many fields such as biochemistry, medicine, and the environmental sciences. An accurate measurement of pH values is essential to the application of science and technology. Reliable pH values are usually obtained by potentiometric measurement using glass electrodes  or ion-set al. reported that the stability and accuracy of measurement can only be secured by both preventing the ageing effect and constructing a fresh junction on the LJ  in the sample solution.As the figure shows, an overshoot potential towards the higher pH direction appeared when the KCl concentration in the BLS exceeded that in the sample solution, and m-r) between the broadened mouth and the reference pH-FET. When dm-r was 0, that is, when the measurement and reference pH-FETs were both placed at the mouth of the cut cannula . Therefore, the potential of the chloride ion-sensitive pseudo reference electrode (Ag/AgCl) was presumed to have changed. At dm-r = 10, 20, 30 mm, the potential change of the reference pH-FET was almost zero. This suggests that the reference pH-FET remained inside the BLS both before and after the sample suction. In conclusion, The potential of the reference pH-FET itself against the pseudo reference electrode changed by 1.9 pH (103 mV) at dWe estimate that the LJP is produced by the following mechanism. From The microfluidic technologies in the present sensor probe could be used not only to miniaturize the external form, but also to modify the fluid flows. This type of application would require an improved mixing profile and the leeway to dramatically reduce the LJP. Many technologies related to two-fluid mixing in microchannels have been described ,44. The We have used microfluidics principles to improve pH measurement performance in a flow-through-type differential pH sensor probe composed of pH-FETs. Specifically, we exerted spatiotemporal control over the LJ of two solutions by vibrating the border region between the solutions and actively mixing them. The vibration and mixing were achieved simply by manually pushing and releasing the tube of the solution-delivery pump. By using of this method, the overshoot of the sensor signal caused by the LJP settled down immediately the sample solution was suctioned in, and the measurement error was reduced to only 0.060 pH even with a sample solution with a low buffering capacity or low ion concentration. The new method may be applied to many potentiometric measurement apparatuses using salt bridges  and is expected to improve the settling time, measurement accuracy, and other response properties. In the future we hope to understand the exact mechanism by which the active mixing eliminates the LJP in the micro scale region. A fuller understanding of the microfluidics phenomena at work may allow us to construct even smaller measurement devices with shorter measurement times and higher measurement accuracy.


Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. In the past few years, the novel effective drugs come out slowly although there is a substantial investment into the development of drugs. It is common for the pharmaceutical industry to develop novel drugs targeting a certain target. However, the once dominating paradigm of “mono drug mono target” in drug development is now being challenged by the clinical and pharmaceutical people, since the single drug cannot always be effective for the complex diseases (such as cancer and diabetes), which may involve multiple biological pathways and complex pathological process. Therefore, the drug combination, which consists of multiple drugs , is now becoming a novel strategy to combat complex diseases –3.It is impractical to screen all possible drug combinations experimentally since there will be an exponential explosion when the number of single drugs increases. Therefore, a great number of computational methods have been recently developed for prediction of drug combinations –7. In geAlthough these existing methods can predict novel drug combinations or provide mechanistic insights into existing ones, they are limited by their efficiency when the size of combination space increases at an exponential growth rate . Therefore, it is necessary to develop prediction methods that are scalable to data and computation. The Hadoop MapReduce system –10 reprehttp://www.cls.zju.edu.cn/dcdb/) [All the basic information about single drugs and effective drug combinations was extracted from the Drug Combination Database (DCDB) (n/dcdb/) . In totahttp://www.broadinstitute.org/cmap/) [http://www.broadinstitute.org/gsea/msigdb/) [In order to encode the drug combinations, we focus on the possible effect of different drug combinations on the pathways that they may be involved in. The gene expression profiles of the 1309 small-molecule drugs or compounds were downloaded from the Broad Institute Connectivity Map Build02 (g/cmap/) , and theg/cmap/) . The annmsigdb/) . We finaFor the fact that we can only directly obtain the gene expression data of single drugs, we should first represent the feature of pairwise (or multiple) drug combinations. In this study, we applied two different strategies to define the combination feature described as below.Di in the drug combination , the expression data of gene Gi is denoted as Pi if it is not affected by drug Di, and denoted as Ci, if it is affected by drug Di. Thus, the combination effect of the pairwise drug combination of D1 and D2 on Gi is defined as(1) This first kind of representation is a direct way to define the combination feature as a linear function of single drugs. For a drug Obviously, this is a simple way to get the combination feature of any pairwise drug combination. However, the representation cannot convey the intricacy of drug combinations due to the complexity of human disease mechanism. t-test for each single drug to get the significantly changed gene set and then mapped them into 186 human disease pathways. This method is finally compared with Zhao et al.'s definition [(2) Instead, we try another way to find the frequent feature pattern of effective drug combinations and take them as the feature of potential effective drug combinations. Here, we assume that a pathway is affected if there exist genes in this pathway whose expression level is significantly changed under the effect of a single drug. We first performed the Student'sfinition , which dThe feature construction method brings high dimensional feature space on a dataset with small size of samples. To avoid the overfitting, we applied several feature selection methods on our dataset. For the first type of feature construction method mentioned above, we performed the minimum-redundancy-maximum-relevance (mRMR)  to selec c for outlier samples and gamma g in kernel functions. There is no smart algorithm to select the best parameters in the training stage, and we searched the optimal parameters using grid search. The search range of the parameters (c and g) is from 0.03125 to 32, with the step as 0.00001. The second type of classification method we used here is the naïve Bayesian algorithm, which can be suitable to be parallelized. In the later section, we will introduce how to implement the MapReduce version of the naïve Bayesian algorithm on the Hadoop platform.In the model building step, we employed two popular machine learning algorithms, support vector machine, and naïve Bayesian to train a classifier for predicting effective drug combinations. In the SVM algorithm, the selection of kernel function and related parameters will have a great effect on the performance of the trained classifier. In the training stage, we compared four types of kernel functions: linear kernel, polynomial kernel, Gaussian kernel, and tangent kernel. The SVM classifiers were implemented by using LibSVM package . There aFor scalable implementation of our mining process, we used the machine virtualization to build the Hadoop cluster. The master virtual machines included 4 Intel core i3 processor cores and 4 GB RAM and the two slave virtual machines with 2 Intel core i3 processor cores and 2 GB RAM. The software environment includes Hadoop-1.2.1, Hive-0.11.0, and RHadoop (an integration of R and Hadoop).After building the scalable Hadoop cluster, we exploited the Hadoop distributed file system to store the raw data and used hive as data ETL tools for relational database and program to process the local files.The feature construction stage can be regarded as a series of independent similar processes on different samples and features. In the Hadoop, we implemented a chain mapper to parallelize the processes, including the gene expression preprocessing and the construction of the proposed drug combination features.For the SVM algorithm, it is difficult to implement the parallel version. Here, we only parallelized the grid search of the optimal parameters, which are time-consuming in the sequential implementation.For the naïve Bayesian algorithm, the implementation of the scalable version using MapReduce is mainly composed of three steps shown in : the cal F-measure (F1). These measures can be calculated by the numbers of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) for each classifier [A tenfold cross-validation and leave-one-out cross-validation test were used to evaluate the classification performance. To assess the performance of the classification models, we used the accuracy (ACC), sensitivity , specificity (SP), andassifier –21. ThesThe performance of the prediction model using SVM algorithm is determined by the representation of the features, the type of the kernel function, and parameters. Here, the tenfold cross-validation test was conducted to evaluate the model performance. We employed three ways of feature representation, including the linear addition, Zhao's frequent pattern , and ourIn this section, we evaluated the prediction performance using our proposed frequent pattern and the Gaussian function on the independent test, which is mimicking a true prediction since the model trained on one dataset is used to test on an unseen dataset. We randomly split the whole set of the 76 drug combinations into two datasets (a training set and a testing set). The ratio is about 4 : 1 between the number of the samples of the training set and that of the testing set. The split of the dataset and the independent test is repeated for 10 times. The performance of the 10 runs and their average is presented in In the task of the two-class classification, the assignment of the negative samples (noneffective drug combinations) is not perfect since the unknown pairwise drug combination (we now consider it as noneffective drug combination) may be proved to be an effective drug combination in future. To avoid this problem, we constructed the one-class SVM classifier trained on the dataset with only effective drug combinations. We made use of leave-one-out cross-validation to assess the accuracy of one-class SVM classifiers using different types of kernel functions. As shown in In this section, we constructed a scalable version of the mining tool for identifying the effective drug combinations and compared its efficiency to that of the sequential implementation by the traditional way. The preprocessing steps  were parallelized by a chain of mappers. The naïve Bayesian algorithm is implemented by a series of MapReduce jobs.The detailed comparison results of our scalable version and the sequential version in efficiency are listed in In this study, we proposed a novel Hadoop-based approach to predict drug combinations by implementing the support vector machine and naïve Bayesian classifiers using the MapReduce programming model, which can advance the improvement of scalability of the prediction algorithm. We believe that our proposed model can be potentially useful when more than two drugs (the increasing availability of the number of the drug combination) are combined for combating the complex diseases in the long run.


Ultrasound imaging is a very essential technique in medical diagnosis due to its being safe, economical and non-invasive nature. Despite its popularity, the US images, however, are corrupted with speckle noise, which reduces US images qualities, hampering image interpretation and processing stage. Hence, there are many efforts made by researches to formulate various despeckling methods for speckle reduction in US images.In this paper, a subspace-based speckle reduction technique in ultrasound images is proposed. The fundamental principle of subspace-based despeckling technique is to convert multiplicative speckle noise into additive via logarithmic transformation, then to decompose the vector space of the noisy image into signal and noise subspaces. Image enhancement is achieved by nulling the noise subspace and estimating the clean image from the remaining signal subspace. Linear estimation of the clean image is derived by minimizing image distortion while maintaining the residual noise energy below some given threshold. The real US data for validation purposes were acquired under the IRB protocol (200210851-7) at the University of California Davis, which is also consistent with NIH requirements.Experiments are carried out using a synthetically generated B-mode ultrasound image, a computer generated cyst image and real ultrasound images. The performance of the proposed technique is compared with Lee, homomorphic wavelet and squeeze box filter (SBF) in terms of noise variance reduction, mean preservation, texture preservation and ultrasound despeckling assessment index (USDSAI). The results indicate better noise reduction capability with the simulated images by the SDC than Lee, Wavelet and SBF in addition to less blurry effect. With the real case scenario, the SDC, Lee, Wavelet and SBF are tested with images obtained from raw radio frequency (RF) data. Results generated using real US data indicate that, in addition to good contrast enhancement, the autocorrelation results shows better preservation of image texture by SDC than Lee, Wavelet and SBF.In general, the performance of the SDC filter is better than Lee, Wavelet and SBF in terms of noise reduction, improvement in image contrast and preservation of the autocorrelation profiles. Furthermore, the filter required less computational time compared to Lee, Wavelet and SBF, which indicates its suitability for real time application. Ultrasound (US) imaging is one of the most commonly used medical imaging due for diagnostic purposes to its many advantages such as portability, the noninvasive nature, relatively low cost and presents no radiation risk to patient. These features have made the US imaging as the most prevalent diagnostic tool for health practitioners over other more sophisticated imaging techniques such as CT scan, MRI or PET. Unfortunately, like SAR, US images exhibit a speckle pattern and its statistical model is identical to single-look SAR amplitude signals. Speckle in ultrasound has adverse effect in such a way it causes reduction in image contrast resolution. In , Bamber An US machine works by introducing into the body of interest a low-energy pulse of sound with frequencies typically between 3 and 30MHz by a transducer probe that touches the patients’ skin surface. Upon travelling through the body tissue, some of the pulses get attenuated while some small portion of the pulse energy are scattered back to the probe. The scattered pulse is then received by the same probe to produce echo signals which are processed to form two-dimensional images, also known as sonogram. This two-dimensional anatomical maps are called B-mode (brightness) images .In principle, US images provide information about internal tissue structures which resulted from interaction between anatomical tissues with the transmitted ultrasound pulse. Due to interaction between ultrasound waves with tissue, backscattered echo signals are produced, in the form of reflection, scattering, interference and absorption. These echo, resulted from coherent summation of ultrasound scatterers, carry information about the tissue under investigation. The nature of coherent summation of such signals gives rise to an interference pattern known as speckle .The despeckling techniques applied in US and SAR imagery can be classified into four main groups, namely, linear and non-linear filters, adaptive speckle filters, wavelet-based filters and anisotropic diffusion-based (AD) approach. In linear filtering technique , 5, the The nonlinear filters are possible alternative to the standard linear filters, and the most popular one is the median filter. It has the advantage of preserving edges and is very effective at removing impulsive noise. The median filter sorts the intensities in the neighbourhood window of the reference pixel and calculates the median value of the sorted data. The denoised pixel is obtained by replacing the original reference pixel value by the median value calculated for the particular neigbourhood window –8. The mthresholding the small coefficients that are likely due to noise. The wavelet-based denoising techniques involve three major steps, 1) perform a 2-D wavelet transform, 2) modify the noisy coefficients using a shrinkage function, and 3) perform a 2-D inverse wavelet transform  is the expected value, the optimum linear estimator can be obtained by solving the following spatial-domain constrained optimization problem [where  problem , 2811σ is a positive constant.where H is a stationary feasible point, if it satisfies the gradient equation of the Lagrangian,The optimum estimator is the sense of Eq.  can be fλ≥0 is the Lagrange multiplier, andwhere HL=0, thus we obtainThe solution to Eq. thus,I is the identity matrix. Hence, the solution for the optimum estimator HSDC is given asSince the noise is assumed to be white, then HSDC is considered, it is worthy to note that there is a strong empirical evidence indicating that the transformed covariance matrix of most images by the eigenvectors of the RX have some eigenvalues small enough to be considered as zeros. This means that the number of basis vectors for the pure image is smaller than the dimension of its vectors.Before the final form of the optimal estimator To verify this key statement, we plot the eigenvalues of two ultrasound images of captured from a patient, as shown in Figure RX are close to zero, which indicates that the energy of the clean image is distributed among a subset of its coordinates and the signal is confined to a subspace of the noisy Euclidean space. Since all noise eigenvalues are strictly positive, the noise fills in the entire vector space of the noisy image. In other word, the vector space of the noisy image is composed of a signal-plus-noise subspace and a complementary noise subspace. The signal-plus-noise subspace or simply the signal subspace comprises vectors of the clean image as well as of the noise process. The noise subspace contains vectors of the noise process only. Using eigendecomposition of RX=UΔXUT, Eq. .Apply the homomorphic transformation to the noisy image, Estimate the noise variance, r.Compute the dimension of signal subspace, r in step 3, apply eigendecomposition on U1, and their related eigenvalues Using the estimated λ, then compute the optimum linear estimator, Select the best value of Compute the clean image, Reverse the homomorphic effect by taking the exponential of the When dealing with ultrasound data, the SDC is implemented in homomorphic framework where the noisy image is first log-transformed prior to SDC filtering. This transformation will convert the multiplicative nature of the speckle to an additive on. The final form of the despeckled image is recovered by performing antilog on the output of the SDC filter. The implementation detail of SDC are given as follows, In essence, reversing the homomorphic effect in step 7 converts the logarithmic form of the filtered image to a linear form prior to image display.λ value for SDC, a test image made up is created as shown in Figure λ ranging between 1 and 105. The signal-to-noise value (SNR) calculated asTo find the best where MSE represents the mean-square error, given byis used to indicate the denoising effect of the SDC. The results are shown in Figure λ may results in oversmoothed images and cause loss of details. Consequently, the rule of selecting λ is that for noise variance less than 0.04, λ should be selected to be around 10 and with noise variance greater than 0.04 it should be selected to be less than 40.The results in Figure The experimental results presented in this section can be divided into 2 parts. In the first part, the performance of the proposed SDC technique is compared with Lee , homomorThe value of 255 in Eq.  correspoIn the second part, the performance of the proposed SDC technique is investigated using a computer generated image and real US images. Here, the Lee filter is implemented with 7×7 window size, the homomorphic wavelet is used with Daubechies length-eight filter and a 7×7 window and the SBF technique is implemented according to the set up given in . The SDCMean Preservation: A good speckle filter will maintains the mean intensity within a homogenous region.Normalized Variance: The normalized variance indicates the performance of the filter in homogeneous areas. This metric is given by When using computer generated US or real US images, the noise-free image is not available which is the practical scenario of denoising applications of US images. Therefore, reference-free methods are used to quantitatively assess the denoising performance. The reference-free methods in this work are mean preservation, normalized variance, autocorrelation  and USDS3.Autocorrelation: is another method of filter assessment in homogeneous area where close autocorrelation profile to the original image indicates better texture preservation. The autocorrelation for m×n image X is given as [given as  25where X is the grey value of pixel .4.Ultrasound Despeckling Assessment Index (USDSAI): is a modified Fisher discriminant contrast metric [t metric . USDSAI where Ck| denotes the number of pixels in class Ck. If a despeckling filter produces classes that are well separated then the numerator in 26 will be large. Conversely, if the intraclass variance is reduced, then the denominator will be small giving large value of USDSAI indicating desirable image restoration and enhancement.where |In this experiment, the capability of the SDC technique in reducing the speckle noise is tested and compared with Lee, homomorphic wavelet and SBF technique. The performances of the noise reduction techniques are measured in terms of PSNR values as tabulated in Table The results in Table In this experiment, the computer model of a cyst phantom is generated using the MATLAB Field II simulation , 39. TheIn the first experiment, the normalized variance and mean preservation for the cyst image are calculated over two selected regions labeled as A and B as in Figure In addition to variance reduction, the values of mean preservation for the two regions calculated before and after denoising for SDC, Lee, Wavelet and SBF filter are also evaluated and included in Table In order to assess the capability of the different algorithms in texture preservation in the denoised image, the autocorrelation in region A and B of the cyst image in Figure ®; System. In order to obtain the B-mode ultrasound images, the URI Offline Processing Tools (URI-OPT) run on MATLAB platform is used to convert the RF data to the B-mode images as shown in Figure In this experiment, the performance of the proposed SDC is analyzed and compared with Lee and Wavelet using ultrasound images captured from a patient as shown in Figure In the first part of this experiment, two homogeneous areas are selected and marked as region A and B Figure In addition to the noise reduction capability addressed by the normalized variance, the mean preservation capability is also tested and presented in Table To gain more insight into the performance of the three considered techniques, their capability in preserving the characteristics of the original image is tested in terms of autocorrelation profiles of the selected region, A and B as shown in Figure In the third experiment, the required computational time by Lee, Wavelet, SBF and SDC to process the ultrasound images of Figure A subspace-based denoising technique for US images is presented and tested. The proposed technique, SDC is based on linear estimator and rank reduced subspace model to estimate the clean image from the corrupted one with speckle noise. The performance of the SDC is tested with simulated and real data, and compared with Lee and wavelet. The results indicate better noise variance reduction capability with the simulated images by the SDC than Lee, Wavelet and SBF in addition to less blurry effect. With the real case scenario, the SDC, Lee, Wavelet and SBF are tested with images obtained from raw RF data. The performances are calculated in terms of noise reduction, improvement in image contrast and preservation of the autocorrelation profiles. The results indicate that SDC offer better texture preservation, measured in terms of autocorrelation profiles and good contrast enhancement, measure in terms of USDSAI value. Finally, the computational complexity of the algorithms is compared and the results show that SDC required the least computational time compared to Lee, Wavelet and SBF.


A new model of how the brain learns beneficial behavior from rewarding outcomes emphasizes the importance of the striatum, replicates experimental data, and raises new questions about neurological disorders. Read the Research Article. A key component of survival is learning to associate rewarding outcomes with specific actions, such as searching for food or avoiding predators. Actions are represented in the cortex—the brain's outer layer of neural tissue—and rewarding outcomes activate neurons that release a brain chemical called dopamine. These neuronal signals are sent to the striatum—the input station for a collection of brain structures called the basal ganglia, which play an important role in action selection. Collectively, this evidence suggests that dopamine signals change the strength of connections between cortical and striatal neurons, thereby determining which action is appropriate for a specific set of environmental circumstances. But until now, no model had integrated these strands of evidence to test this widely held hypothesis.PLOS Biology, University of Sheffield researchers Kevin Gurney and Peter Redgrave teamed up with Mark Humphries of the University of Manchester to build a computational model that shows how the brain's internal signal for outcome changes the strength of neuronal connections, leading to the selection of rewarded actions and the suppression of unrewarded actions. By bridging the gap between the intricate subtleties of individual neuronal connections and the behavior of the whole animal, the model reveals how several brain signals work together to shape the input from the cortex to the basal ganglia at the interface between actions and their outcomes.In a study published this week in The researchers developed a network model of the whole basal ganglia based on previous electrophysiological studies that investigated the activity of two types of dopamine-responsive cells called D1 and D2 striatal medium spiny neurons. In addition to this action selection model, they developed an independent plasticity model by incorporating experimental data from a previous study to show how the strength of neuronal connections, called synapses, is affected by three factors: the timing of neuronal activity, the type of medium spiny neuron, and dopamine level. Then they linked the two models, testing whether plasticity rules at single synapses between cortical and striatal neurons could give rise to the predicted changes in the activity of the two types of medium spiny neurons, resulting in successful learning of the association between actions and outcomes.Achieving a remarkable convergence between vastly different scales of space and time, the computational framework not only replicates experimental data on cortico-striatal plasticity but also accounts for behavioral data on learning the association between actions and outcomes . The modAccording to the dominant conceptual model of the basal ganglia, D1 and D2 medium spiny neuron populations project through two competing pathways that either promote or suppress an action. This model has been used to explain the motor symptoms of Parkinson's disease, Huntington's disease, and other neurological disorders associated with basal ganglia dysfunction. However, the new network model revealed that D1 and D2 medium spiny neurons coding the same action cooperate to produce optimal action selection, suggesting that the dominant model is not true and needs to be reevaluated.Taken together, the results of the study provide strong support for the hypothesis that cortical inputs to dopamine-releasing neurons in the striatum are crucial for learning the association between action and outcome. Moving forward, the model provides a common framework in which to place new findings on all aspects of learning from outcomes. In the clinical realm, it could also reveal novel insights into the mechanisms behind motor disorders and shed light on abnormal learning related to diseases such as addiction. Moreover, the work highlights the importance of the striatum, not just the cortex, in high-level learning that is crucial for survival, possibly explaining why the basal ganglia are highly evolutionarily conserved across vertebrate species.Gurney KN, Humphries MD, Redgrave P (2015) A New Framework for Cortico-Striatal Plasticity: Behavioural Theory Meets In Vitro Data at the Reinforcement–Action Interface.doi:10.1371/journal.pbio.1002034


The human intestinal microbiota changes from being sparsely populated and variable to possessing a mature, adult-like stable microbiome during the first 2 years of life. This assembly process of the microbiota can lead to either negative or positive effects on health, depending on the colonization sequence and diet. An integrative study on the diet, the microbiota, and genomic activity at the transcriptomic level may give an insight into the role of diet in shaping the human/microbiome relationship. This study aims at better understanding the effects of microbial community and feeding mode (breast-fed and formula-fed) on the immune system, by comparing intestinal metagenomic and transcriptomic data from breast-fed and formula-fed babies.We re-analyzed a published metagenomics and host gene expression dataset from a systems biology perspective. Our results show that breast-fed samples co-express genes associated with immunological, metabolic, and biosynthetic activities. The diversity of the microbiota is higher in formula-fed than breast-fed infants, potentially reflecting the weaker dependence of infants on maternal microbiome. We mapped the microbial composition and the expression patterns for host systems and studied their relationship from a systems biology perspective, focusing on the differences.Our findings revealed that there is co-expression of more genes in breast-fed samples but lower microbial diversity compared to formula-fed. Applying network-based systems biology approach via enrichment of microbial species with host genes revealed the novel key relationships of the microbiota with immune and metabolic activity. This was supported statistically by data and literature.The online version of this article (doi:10.1186/s40168-015-0104-7) contains supplementary material, which is available to authorized users. Escherichia coli, the microbial community is later diversified with anaerobes, e.g., Bifidobacterium and Clostridium [The intestinal microbiota and its human host develop a strong mutual relationship . Severalstridium . The facstridium  and incrstridium .E. coli, the microbial community is later diversified with anaerobes, e.g., Bifidobacterium and Clostridium [Despite the fact that bacterial cells outnumber the total number of human cells in the body, the human gut contains a surprisingly limited number of dominant enterotypes . Startinstridium . The facstridium  and incrstridium .A systems biology approach to study the relationships between the host and the microbiota requires the measurement of biomolecular activity within the host  and the quantification of host microbiota under similar conditions . The acq® LIPIL®: a commercially available formula.Our study aims to observe cross talk at microbial and transcript level, in order to understand the effect of feeding mode on the host system, on the microbiome, and on the interaction between the two. We present a detailed analysis of the data studied in , from syOur analysis is an extension of the work of Schwartz et al.  with theOur results indicated the effect of feeding mode both on the microbiota and the expression patterns of certain genes.Actinobacteria showing prominence under both feeding conditions .We used the abundance data to understand the relationship among the microbial species detected in the samples. We computed the Bray-Curtis similarity among the samples see “” to creaBifidobacterium species detected in our analysis, B. breve and B. bifidum were detected mostly in BF infants, whereas B. longum and R. gnavus were detected in FF samples  showed a higher correlation  for B. bifidum and R. gnavus. Figure p values as the two other differentially abundant microbes (B. breve and B. longum) were not detected in both types of sample. We compared these correlation coefficients against the mean of absolute correlation between 1000 randomly permuted microbial species and host gene pairs, which was found to be <0.2. Furthermore, we also generated random pairs of genes and microbes and computed similar correlations (abundance~expression level) to check if the correlations for actual relationships are better than these random pairs. We compared these correlation coefficients against the mean of absolute correlation between 1000 randomly permuted microbial species and host gene pairs, which was found to be <0.2  of the genes related to differentially abundant species indicated their role in the immune system Fig. . SimilarThe analysis of the gene expression data obtained from the epithelial cells in fecal samples of the infants see “” distingCombining the outcomes from the abovementioned results, we could layout the dependence of the microbiota and human system on feeding mode together with the relationship between the microbiota and the human system  mining the relationships between the microbiota and the host genes from literature and their validation with the gene expression and microbial abundance data; (2) a differential abundance analysis of the microbiota at species level; and (3) combining these findings at a systems biology level depicting the overall relations between the feeding mode, the microbiota, and host biomolecular activities.Bacteroides in BF samples was unusual, but it can be explained, as recent findings have shown that Bacteroides colonize infant gut with time [Bacteroides in BF samples. Furthermore, it has also been shown that certain Bacteroides species can use the sugar in human milk via mucosal layers [B. longum was abundant in the FF infants. B. longum is one of the widely used probiotic in formula food [Feeding mode has been shown to affect the microbiota composition as well as gene expression in infants , 14, 21.ith time , 33. Thel layers . It has l layers . Our finl layers –22. Neveula food . The genThe picture was different at the level of human genes. The gene network among the BF sample was found to have a higher number of interactions (~2.1 times the FF network). Thus, the phenotypic outcomes observed in the host system are affected by feeding conditions. The BF samples do not exhibit a great diversity in the microbiota network . The higThe conservation of the core of microbial co-occurrence networks and the differentially abundant microbes being associated with genes that already have higher degree, leads to the question of the true functional importance of diversity in the microbiota. However, a core microbiota is undoubtedly required for optimal health  and it hOur findings based on small but unbiased samples leveraged by robust statistical checks suggested that the development of human immune system during infancy should not be seen as an effect of the diet or the microbiota at individual level, rather these factors should be studied together in a system level approach. Our results indicated how feeding affected the expression pattern of genes as well as the microbial community and how these two factors together impact the host system (infants).We used a network model with multiple components  to study the effects of feeding conditions in infants on the microbiota and on the overall human system and its role in host immune system. The components included the microbial system and the host genes together with the interplay between these components. The analysis on the microbiota revealed a feeding mode-dependent difference in terms of diversity of the microbiota as well as of the network describing the interactions among them. The comparison of the results based on literature and data showed the validity of our findings. The network among the differentially expressed genes, computed via co-expression, was found to be much denser in the case of BF infants than FF infants, depicting higher biomolecular cross talk depending on the feeding mode. Many of the edges in the gene network for BF and FF samples could be mapped onto immune, signaling, or metabolic pathways. Mapping the relations between microbes and human genes was another major advancement achieved during the study. We complemented and extended the work of Schwartz et al. by detecting the differences in the microbiota based on feeding mode. The study of the microbiota and the host system together helped us to study a meta-system consisting of the host with the fellow microbes residing within the host rather than individual organism systems. Our results revealed that the integration of -omics data from the host and the microbiota with existing knowledge  could yield useful insights into the system or rather the meta-system.We used (1) metagenomic and (2) transcriptomic data , 43 fromOur approach aimed to use the metagenomic and transcriptomic data integratively in order to infer the relation between the microbiota, the host (human) system, and the feeding mode.p value cutoff of 0.05 (after multiple testing correction based on Benjamini-Hochberg method [The limma package  from Biog method ) and a lWe analyzed the metagenome data in the form of sequence reads in order to obtain the enrichment of microbes in the samples. We used Bowtie 2  to alignInferring microbial community network is a major challenge due to ambiguities about the operational taxonomic unit (OTU) definition and corresponding variation in quantification data based on OTUs. However, we define “microbial species” as the OTU in our investigation for simplicity and in order to follow the standards in the literature. The abundance table . The cutoff for the correlation was set to 0.8. For simplicity, unweighted networks were constructed. The networks were joined with the microbial community networks and the results from text mining procedure to get an overall picture.We did a hyper-geometric test on the genes in the data from different sources  to understand the biological roles of the human genes involved by means of a functional enrichment of these gene sets. A detailed analysis of this functional enrichment was done at individual and combinatorial level. The differentially expressed genes and text mining genes underwent functional enrichment with GO Biological Process terms  and Reac


Mallory-Weiss syndrome (MWS) accounts for 6–14% of all cases of upper gastrointestinal bleeding. Prognosis of patients with MWS is generally good, with a benign course and rare recurrence of bleeding. However, no strict recommendations exist in regard to the mode of action after a failure of primary endoscopic hemostasis. We report a case of an 83-year-old male with MWS and rebleeding after the initial endoscopic treatment with epinephrine and clips. The final endoscopic control of bleeding was achieved by a combined application of clips and a nylon snare in a “tulip-bundle” fashion. The patient had an uneventful postprocedural clinical course and was discharged from the hospital five days later. To the best of our knowledge, this is the first case report showing the “tulip-bundle” technique as a rescue endoscopic bleeding control in the esophagus. Mallory-Weiss syndrome (MWS) refers to a bleeding from vomiting-induced tear of the mucosa at the gastroesophageal junction or gastric cardia. The MWS causes approximately 6–14% of all causes of upper gastrointestinal bleeding . MoreoveIn patients with the MWS and active bleeding or exposed vessels, the endoscopic hemostasis is warranted. Previous studies have confirmed the effectiveness of several endoscopic techniques, that is, epinephrine injection, hemoclip application, and band ligation , 6. HoweCombined use of hemostatic clips and detachable nylon snare (the “tulip-bundle” technique) has been described as an effective therapy for the closure of esophageal perforations after endoscopic resection  and of eAn 83-year-old man with the ischaemic heart disease, gastroesophageal reflux disease, and previous peptic ulcer bleeding was admitted to our hospital with a history of haematemesis and melena. At the time of presentation, he was hemodinamically stable, and initial laboratory findings were normal. Urgent upper endoscopy revealed multiple mucosal tears above and at the gastroesophageal junction. The tear above the junction was with the active bleeding. The bleeding was arrested with combined application of epinephrine and endoclip . Further treatment included intravenous administration of fluids and proton pump inhibitors, with nihil-per-month restriction.Seven hours after the procedure, the patient re-presented with retching and vomiting the fresh blood, thus prompting a second upper endoscopy. The clot in the esophagus was observed at the site of the primary hemostasis . After rEndoscopic hemostasis with clips or thermocoagulation is the current standard in the management of the nonvariceal upper gastrointestinal bleeding . DespiteWith regard to the nonvariceal upper gastrointestinal bleeding, Lee et al. have examined the role of the tulip-bundle technique as a rescue treatment after the previous endoscopic treatment failure . A totalThere were a couple of reasons behind our decision to use the same approach in our patient. This was an octogenarian, with a significant comorbidity, all of which are recognised risk factors for a fatal outcome in patients with the MWS and in whom emergency surgery would be very risky . HoweverTo the best of our knowledge, this is the first case report showing the tulip-bundle technique as a rescue endoscopic bleeding control in the esophagus. Notwithstanding the fact that our case adds to the current body of knowledge in regard to effective control of nonvariceal upper gastrointestinal bleeding; the feasibility of this approach will require future studies on a large number of patients.


Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential  can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives  and false positives . In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems. The remarkable growth of life expectancy has multiplied the number of senior citizens that face daily the risks of living on their own. Although it is well known that physical exercise avoids or delay the onset of diseases, it can also lead to falls, the major health hazard that diminishes the quality of life. Data from the World Health Organization , the appThis paper presents the prototype of an experimental system for fall monitoring. The prototype combines an Android-based smartphone as the platform hardware, a motion sensor  and the location services supported by the smartphone. The election of a mobile phone-based system has evident advantages. On one hand, mobile phone-based applications can operate almost everywhere because of the popularity, decreasing costs and portability of mobile devices and the ubiquity of mobile technologies. In fact, the use of smartphones has grown to become a basic constituent of daily routine. Besides, most current smartphones seamlessly integrate all the required elements (accelerometers and gyroscopes) to develop autonomous and self-sufficient fall detection applications. An important point in the design of any healthcare monitoring application is ergonomics. Wireless communications clearly improve patients' mobility while the reutilization of an already existing personal device avoids the annoyances of carrying a separate fall detection gadget. Thus a smartphone oriented system does not introduce any specific attachable component in the life of older people, who in turn are becoming less reluctant to admit novel technologies to improve their safety and independence.This paper is organized as it follows: after the introduction of this Section 1, Section 2 revises the related works. Section 3 presents the general structure and objectives of the developed system. Section 4 describes the design of the detection algorithms to be tested whereas the global system architecture and implementation are presented in Sections 5 and 6. In Section 7, the performance of the system and the accuracy of the acceleration-based detection algorithms are evaluated by means of extensive experiments performed on different scenarios. Finally, Section 8 draws the main conclusions of the work.2.Due to the advances in the area of electronic sensors and the widespread extension and cost reduction of personal devices, the research on systems for fall detection has rocketed during the last decade. Recent comprehensive works have thoroughly addressed the state-of-the-art on fall detection systems. In this sense, different criteria have been proposed to categorize the existing proposals. For example, the authors in  differenetc. For example, the systems by Anderson etc. For Anderson , Jansen Anderson , Lee etc. For Anderson , SixsmitAnderson , VishwakAnderson  or Yu [2Anderson  propose Anderson . Apart fAnderson , anotherAnderson , as theyIn any case, both ambient and vision (or image and audio processing) based schemes apply context aware techniques that require defining a supervision environment where the user activity is supposed to take place. In a similar way, the project described in  presentsConversely, the approaches that employ wearable devices incorporate specific garments with embedded sensors (accelerometers) to estimate the motion  of the user's body in any unsupervised environment. If the wearable garments are also enabled with a wide area communication interface , the user can be ubiquitously monitored. Most smartphone-based fall detection systems can be clearly categorized into this family of “ubiquitous” detection techniques. As a main advantage, smartphone solutions do not constrain the user mobility to a particular monitoring zone while they provide a seamless inexpensive technology that is already integrated in the daily life of most potential users.Nowadays, smartphones incorporate a wide set of embedded sensors, including not only accelerometers, but also cameras, microphones, digital compasses, gyroscopes or GPS units. The rapidly decreasing cost of the smartphones has fostered the adoption of this wearable technology and the measurement of the acceleration as the basis for fall detection. Consequently many smartphone-based architectures have been proposed over the last years.Initial smartphone-based fall detection systems ,29 were PerFallD is the Android architecture developed by the authors of the theoretical study in ,37. The In order to minimize the requirements of computational power and real time processing in the smartphone, other systems, such as that proposed in , just coAnother Android App for smartphone-based fall detection is iFall  , which ii.e., a neural network) is utilized to analyze the user's mobility pattern.The work in  combinesViet  jointly The authors in  introducThe study in  evaluateMost proposed smartphone-based solutions are “smartphone-only” architectures where no supplementary components (apart from the phone's built-in sensors) are utilized . The incThe platform described in  also intAs it can be deduced from the aforementioned studies, accelerometry is, by far, the most extensively employed method for the detection of falls in smartphones. The position where the accelerometer is located  has been used as a criterion to classify the existing solutions based on tri-axial accelerometers . In someClassification of the movement founded on Pattern Recognition Methods (PRM) that employ data bases, training phases and AI  solutions. In the systems proposed by Ganti  and KaraDetection based on acceleration thresholds (Threshold Based Detection or TBD): In  Nyan utiThere are two general strategies to detect the fall occurrences from the data obtained by the accelerometers:i.e., body area networks that include an Android-enabled device . Nevertheless there exist also examples where the Android device is the center of a context aware system  that the revised systems employ is indicated in a particular column of Finally, the fall decision algorithm that is utilized by the system is shown in the last column of the table. The table reveals that simple Threshold-Based Detection (TBD) systems are preferred to complex Pattern Recognition Methods (PRM), which usually impose higher computing and memory costs as well as long training phases to adapt the algorithm to the particular characteristics of the user to be monitored.etc.) do not provide any detailed information about the detection algorithm employed or any insight about the performance achieved by the software. As a matter of fact there is no consolidated Android-based product in this realm . The device is equipped with a volume control and an interphone so that, in case of emergency, it emits a musical tone and a flashing LED-type light signal to alert anyone nearby. However, as in the previous example, this system has evident limitations to achieve a persistent ubiquitous fall detection.Betterbuys  is an ec‐ITT EasyLifeS  is anoth‐Philips Lifeline  consistsAs it refers to commercial systems for fall detection, most existing professional solutions are normally equipped with a specific attachable hardware which is in charge of measuring the user's motion. We can mention the following products:Despite the efforts to achieve a reliable system, all the aforementioned commercial products reveal some kind of deficiency that hinders the efficiency and ubiquity of the fall detection. Firstly, they present a higher cost than smartphone-based solutions. Besides they oblige to wear an additional and specific garment, while communications are normally limited to indoor scenarios and short range transmissions as the use of a base-station is required in many cases. Furthermore, they offer closed solutions that cannot be easily reprogrammed, customized or adapted to the particular conditions of the user or the application. In addition, the typical algorithms employed for the detection cannot be numerically parameterized . In fact, the utilized detection algorithms are not usually described by the vendor in the documentation of the product.3.The main purpose has been to apply mobile technology in health care field by developing and implementing an Android platform-based prototype system, named Monitoring Elderly People with Dementia (MonEPDem) to monitor older people with early dementia. The system is operative for both indoor and outdoor environments and it requires no extra hardware or service cost (apart from those derived from the use of a smartphone).The prototype, which is sketched in Aiming at enabling an efficient and pervasive fall detection, AppPerFallD allows selecting different fall detection algorithms that utilize acceleration threshold-based techniques, which can benefit from the built-in sensors (accelerometer and gyroscope) which are commonly integrated in most present smartphones. In this paper, we utilize the deployed platform to compare the performance of these algorithms.Besides, AppLocationInMaps, which runs on the remote monitoring point , is continuously ready to receive alarm SMS messages. In case of a fall, this Android app displays the most recent (or the last known) location of the monitored user, by plotting it on a map downloaded from the Google Maps web service .4.Our goal is to implement and compare different acceleration-based fall detection techniques proposed by the literature. In particular we consider the following algorithms:4.1.TA|) of the total acceleration of the phone (TA⃗). This module can be computed as:x, AyA and zA are the acceleration readings in directions of x, y, and z-axis measured by the accelerometer that is embedded in the smartphone.For the fall detection this basic algorithm only uses the module .A fall is directly assumed if the measured module of the acceleration exceeds a decision threshold. Thus, the detection decision only considers brusque peaks in the acceleration, neglecting the analysis of the complex behavior of the acceleration vector whenever a fall takes place. As a consequence, this algorithm is prone to the detection of false positives  is suggested by Yoshida [i-th sample of the acceleration, FI can be computed as a function of the 20 last measurements of the acceleration in the x, y, and z-axis :A  Yoshida . For thei.e., without sudden variations of the acceleration) may go unnoticed.A high sampling frequency of the acceleration vector is normally established for a proper computation of FI in the case of sudden falls. However, according to this strategy, most falls that occur slowly (4.3.TA⃗) and the acceleration at the absolute vertical direction (VA⃗), which can be estimated as:θy and θz are the measured pitch and roll values, which determine the mobile phone's orientation. These angles are sensed by the gyroscope integrated in the smartphone.PerFallD  algorithTA| and |VA|. Thus, in order to assess the occurrence of a fall, the algorithm considers two phases for both parameters.The algorithm separately analyses |TA| within an observation time window (ttwin) surpasses a certain triggering threshold (ttTh), the pattern recognition phase is initiated. During this second phase the difference between the maximum value and the minimum value of |TA| is computed within a second checking time window (ctwin) after ttwin. If this difference does not exceed another threshold (ctTh), a possible fall is considered to be detected. A similar process is applied to |vA|, with the corresponding time windows tvwin and cvwin and the thresholds tvTh and cvTh. A fall is only assumed to have occurred if both detection conditions about |TA| and |vA| are satisfied.If the difference of the estimated value of |4.4.TA| crosses a lower and an upper threshold during a certain observation time window, a fall is suspected. However, the fall is only reported if the patient really begins from an upright position and ends in a horizontal position. For that purpose, if the vertical position is restored (or if a dropped smartphone is picked up) within a “post-fall” observation period, the detection event is neglected. Otherwise, if the vertical position is not recovered before this time expires, the system assumes that the patient is lying on the ground and the alarm is emitted.This algorithm  takes in5.etc.) and the personal data and preference of the user . These parameters of the profile are fully configurable by the user.The general workflow of the developed program is illustrated in After the program is parameterized, the monitoring process is launched. Thus, the real-time data collected by the accelerometers are permanently compared to the detection thresholds according to the selected algorithm . If the preset thresholds are surpassed , a “stationary phase” is initiated to confirm that a fall may have occurred. This phase corresponds to the “pattern recognition phase” and the “post-fall” observation period of the PerFallD and the iFall algorithms, respectively. On the contrary, the duration of this phase is set to 0 if these algorithms are not considered.When the stationary phase concludes , another timer is executed. During this new period, the smartphone emits an acoustic alarm to inform the user that a fall has been detected. If no response from the user is received through this time (a screen button is not touched), a fall is assumed and the alert notification is triggered. In this case, the application obtains the GPS coordinates of the user and a timestamp. Depending on the configuration of the program, this information can be directly sent by a text message to a set of predefined contacts (selected by the user in the emergency contact list) or, otherwise, the application can make a phone call to a certain number also specified in the configuration. On the other hand, if the acoustic alarm is turned off before the corresponding timer elapses, the application returns to the normal monitoring process.6.The prototype is initially developed on a HTC Desire X smartphone. This device features an ARM-based architecture, dual-core CPU working at 1 GHz, with 768 MB RAM memory, a 10.16-cm screen, GPS sensor and 4 GB of internal storage. It is powered by a 1650 mAh rechargeable lithium ion battery and incorporates an embedded accelerometer/G-sensor. The OS version employed by the phone is Android 4.0. The system is put into operation by the two abovementioned software applications: AppPerFallD, which implements the detection algorithms, and AppLocationInMaps (in the remote monitoring point). Both programs are implemented in Java, with Eclipse and Android Development Tools (ADT) plugin. The system was also installed and tested on an HTC Sensation XE model, provided with similar sensors, a 10.922-cm screen and Android 2.3.4 Gingerbread OS.MonitoringPerFallD: It includes a UI (User Interface), which is designed for older people by following the elderly-friendly design ideas from Jitterbug . Thus, iDetection Service: It is the monitoring service that implements the fall detection algorithms. To execute these algorithms, the service is in charge of collecting and recording the readings of the sensors. These readings are processed basing on a power-aware strategy.Other four specific modules handle the rest of the functionalities of the application: the transmission of fall alerts, the smartphone connectivity (via Wi-Fi or UMTS), the location processing and the management of a SQLite Database to store the monitored user location.The two main software modules of AppPerFallD are:Additionally, AppLocationInMaps is the application developed for the remote monitoring point. Its goal is to receive, decode and present the information contained in the alert messages that AppPerFallD transmits when a fall is detected. Among other functions, the application displays the patient's location on a map downloaded from Google Maps web service.7.The analysis focuses on the performance of the implemented fall detection algorithms as well as on the resource consumption of the application.The algorithms were evaluated by a series of methodical experiments. Thus, a set of different movement patterns  are simulated by 15 different volunteers  in an indoor environment (a domestic living room). The subjects emulated the falls according to three directions , at different speeds and over a pad to reduce the impact. The rest of simulated movements consist of diverse “Activities of Daily Living” (ADL) such as jogging, walking, standing, sitting or answering the phone. Experiments were repeated by changing the position where participants placed the smartphone: attached to the waist (by means of a belt) or next to the thigh (within a trouser pocket). Each individual carried out more than 50 movements  for every algorithm and every position under test.i.e., those falls that remained undetected) and false positives . The estimation of the false positives does not take into consideration the possibility that the user can cancel the alerting process after a fall is detected and the local acoustic alarm is triggered in the smartphone . The selected thresholds for the algorithms were also the same selected in the tests investigated in [In order to evaluate the ability of the algorithms to discriminate the fall detection patterns, we computed the number of false negatives . These settings are selected basing on training data and aiming at minimizing the false negatives while reducing the false positives to a reasonable minimum.For comparison purposes, we set all the thresholds and time windows of the algorithms to the same values utilized in the bibliography ,43,106. i.e., the fall direction) is modified. In this case, results  suggest that the typology of the tested falls is a key aspect when assessing the capability of the system to detect the fall event. In any case the benefits of using certain algorithms are not as evident as those reported in other studies, such as [These results show that PerFallD and iFall algorithms offer better results than the basic “thresholding” methods . We think that this is due to the fact that PerFallD and iFall algorithms assume a more complex and realistic fall pattern with at least two phases and a certain “observation window”. This observation window is also defined by Fall Index (as long as it takes into account the evolution of last 20 samples of the acceleration components). In fact, the Fall Index algorithm exhibits relatively good results just basing its detection decision on the evolution of the changes in the global acceleration during a small time interval. The algorithms that incorporate a longer analysis of the user activity before a fall is assumed also reveal a more homogeneous behavior when the fall pattern  and false negatives (FP) must be achieved. To illustrate the importance of this trade-off, the scatter plot in A crucial aspect when evaluating a smartphone oriented software is power consumption. Complex computation or massive operation of the sensors may cause heavy battery consumption and make monitoring applications virtually infeasible from a practical point of view.etc. To isolate the impact of the fall detection app on the smartphone power consumption, we perform a series of tests in which we compare the battery discharge for different activity conditions of the fall detection system.In general, power drain in smartphones is highly dependent on several features such as electrical and network setting, user location, signal power, user activity, phone utilization, Scenario 1: AppPerFallD runs in passive mode without executing the detection algorithms. Consequently, the mobility of the smartphone does not affect the consumption.i.e., the fall detection algorithms  are activated. This implies that the acceleration values measured by the G-sensor are continuously processed. In this case the smartphone is kept in a completely static position.Scenario 2: AppPerFallD runs in active mode, Scenario 3: The application is also active, but, in this case, the smartphone undergoes a pattern of periodical simulated falls. Consequently, the corresponding alerting SMS messages are transmitted to the remote monitoring point. These SMSs inform about the position of the user. Thus, the GPS coordinates need to be obtained.In particular, for each test, the phone battery is fully charged and, after that, the power state is periodically monitored. No other additional application was executed in the phones during the experiments. Three diverse scenarios are considered:For both smartphone models, results indicate that the monitoring application has a not negligible repercussion on power consumption . The graph for the scenario 3 evidences that consumption can severely increase if the application utilizes updated information from the GPS.8.Smartphone-based architectures for pervasive fall detection can clearly benefit from the massive social acceptation and widespread extension of smartphones. These devices, which natively integrate accelerometers, gyroscopes and diverse communication interfaces , provide a cost-effective and efficient solution for the deployment of wearable systems for fall detection and alerting.This paper has presented a prototype of a fall detection system based on Android applications for mobile phone platforms. The system is in charge of sending a message or automatically establishing a phone call whenever a fall is presumed.i.e., direction) of the falls indicates that any fall detection system must be evaluated through an exhaustive test-plan with a high diversity of movement patterns. The study of the trade-off between “false positives” and “false negatives” also reveals the importance of the selected thresholds, which completely govern the accuracy of the detection process. In addition, the limitations introduced by the battery lifetime may become a remarkable element to determine the viability of this type of fall detection systems in a real application environment where a user should be permanently (24 h a day) telemonitored. The constant use of the accelerometer and (if needed) the GPS sensor by the detection algorithms undoubtedly reduces the autonomy and applicability of smartphone-based architectures . Consequently fall detection Android applications must be carefully designed to optimize the access to the employed sensors and to minimize power consumption.Most works in the literature about smartphone-based fall detection architectures base the identification of fall patterns on the analysis of the data reported by the built-in smartphone accelerometer . Although there are solutions that employ trained AI systems to discriminate the falls from the conventional physical activity of the users, the hardware limitations of the memory and real-time processing capabilities of the smartphones recommend implementing less sophisticated detection procedures. In this sense, the majority of the proposals apply simple threshold-based techniques to process the sequence of acceleration data. In this work, the developed prototype, tested with different smartphone models, was aimed at evaluating different existing algorithms that utilize threshold comparison methods to identify the falls. For this goal, a wide set of experiments executed by 15 volunteers were conducted. Experiments included a mixture of simulated falls and conventional movements. In contrast with the conclusions of other studies , the obtained results reflect the difficulty of determining an optimal strategy to detect falls. For example, no algorithm achieves an efficiency higher than 95% or 90% to avoid false positives and false negatives, respectively. In an actual application environment, this could imply that many falls could be unnoticed while many movements related to regular activities could provoke alarms that should be manually deactivated by the user before an alert message is sent to a remote monitoring point. The strong dependence of the measured performance on the typology (etc.) may noticeably hinder the acceptance of these telemonitoring services.Ergonomics and usability are other two key aspects for the actual adoption of this type of technology . In this sense, the need for frequent interaction of a not-expert user (battery charging, cancellation of false alarms, programming of detection thresholds, complex training phases to characterize the user's activity patterns,