Validation of the proposed algorithm used images of patients who had undergone chest examination with both arms raised up, one arm placed down and both arms placed down, images of the pelvic region consisting of one substantial object, and images of the lower extremities consisting of two separated areas.All CT datasets were retrospectively acquired by the Toshiba Aquilion 128 CT scanner. The proposed algorithm consisted of a contouring stage for the Dw and SSDE as the previous algorithm when images consisted of one substantial body part. However, when images consisted of more than one substantial body part, the new algorithm was able to detect all parts of the patient within the image. The Dw values from the proposed algorithm were 9.5%, 15.4%, and 39.6% greater than the previous algorithm for the chest region with one arm placed down, both arms placed down, and images with two legs, respectively. The SSDE values from the proposed algorithm were 8.2%, 11.2%, and 20.6% lower than the previous algorithm for the same images, respectively.The proposed algorithm gave the same results for Dw and SSDE. The proposed algorithm is more general and gives accurate results for both Dw and SSDE whether the CT images contain one or more than one substantial body part.We have presented an improved algorithm for automated calculation of D CTDI100, which is measured by a 100‐mm‐long pencil ionization chamber, raises questions about its efficiency inwide beam CT.The CTDI metric was initially proposed to improve the multiple scan average dose (MSAD) metrics for quantifying the CT scanner output dose.100 varies across the field of view (FOV). In case of body CT imaging, for instance, the CTDI100 is typically a factor or two higher at the surface  than at the center . The average CTDI100 across the FOV is characterized by the weighted CTDI (CTDIw).vol).vol is measured using a standardized polymethyl methacrylate (PMMA) phantom, either with a fixed diameter of 16 cm to express the patient's head or a diameter of 32 cm and length of 15 cm to express the patient's body.vol reflects a proper and consistent index of scanner output only.The CTDIeff), which can be obtained from both the anterior posterior (AP) and lateral (LAT) dimensions. SSDE is calculated by normalizing CTDIvol with a size conversion factor (fsize) taken from AAPM 204.eff does not account for the attenuation effects of the body's constituent materialsA new dose metric, the size‐specific dose estimate (SSDE), was introducedw) was proposed.w can be calculated either from CT localizer radiographs or from axial CT images. The attenuation values from axial CT images can be calculated directly by averaging the CT number in a region of interest (ROI) containing that object, while calculation from CT localizer radiographs can be performed by properly calibrating the pixel values in terms of water attenuation.w calculation is highly affected by minification or magnification of the LAT dimension due to mis‐positioning of the patient from the iso‐center.w using reconstructed axial CT images provides a more accurate result.An improved, more accurate metric to include both patient size and tissue attenuation, the water‐equivalent diameter  automatically fitted to the patient border. The automated calculation produced an excellent correlation to the manual calculation (R2 = 0.999). Özsoykal et al.w and SSDE based on the Fuzzy C‐means classification algorithm and edge detection. Recently, Juszczyk et al.w and SSDE using a convolutional neural network (CNN) and reported accurate results. The algorithms were effectively designed to calculate Dw based on segmentation of the largest object.w and an overestimate of SSDE. Therefore, we propose an improvement of the automated Dw calculation which will accurately measure SSDE even in thoracic examinations which include the patient's arms and lower extremities examinations which include separated legs. Our algorithm is expected to result in a general and more accurate patient segmentation from images whether they consist of one or more substantial separated objects. Thus, the estimate of patient doses (SSDE) will be more accurate and robust, so that the dose optimization through the diagnostic reference level (DRL) on the basis of SSDE can be increased.An accurate automated approach to finding D22.1This was a retrospective study on datasets from a hospital where anonymization and confidentiality were performed. All datasets were obtained from a multi‐detector computed tomography (MDCT), the Toshiba Aquilion 128, between August 2014 and December 2014. The dataset consisted of axial images of adult patients who had undergone chest, pelvic, and lower extremities CT examinations. The chest datasets were classified into three groups: 40 datasets with both arms raised Figure , 40 data2.2w calculation employed a threshold operation using a HU of −200, edge detection, and setting the largest object as the patient boundary.The flowchart of the proposed algorithm is presented in Figure w. Therefore, instead of using the largest body part as the patient border, the current method uses the six largest body parts. If the image consists of more than one substantial body part, the algorithm successfully detects the edges of all these body parts. On the other hand, if the image consists of only one substantial body part, then the five smaller detected body parts located within the one largest body part will be united into one large body part.For images involving multiple parts of the patient's body, the contouring process based on the largest object produces an inaccurate estimate of Dy‐axis) of the six objects and eliminated the objects whose positions are larger than 400 pixels from the top of the image ROI is the mean HU contained in the ROI. The Dw calculation was performed for all the slices in the scanning range and an average value of Dw was calculated.The area and the mean HU of the image within the automated ROI result  were then calculated. The value of D2.3vol extracted from the DICOM information of the patient with the size conversion factor (fsize) based on Dw obtained from AAPM 204The value of SSDE can be determined by normalizing the CTDIw,n to define water‐equivalent value based on the new algorithm and SSDEn to represent patient dose estimation from the new algorithm) were correlated with the results measured by the previous algorithm  . Both algorithms were effectively integrated into a simple graphical user interface (GUI) of IndoseCT  as depicted in Figure w,n and Dw,o were calculated using Equation ΔDw for both regions are equal to one.Figure w and SSDE based on the old and the new algorithms for images consisting of one substantial object are tabulated in Table w and SSDE, as expected.The results of Dw and SSDE based on the old and the new algorithms for images consisting of more than one substantial object. The correlations of Dw and SSDE values from the old and the new algorithms for the chest region with one arm down are presented in Figure R2 > 0.94 for both Dw and SSDE), for the chest region with both arms down in Figure R2 > 0.91 for both Dw and SSDE), and for separated leg images in Figure R2 > 0.99 for both Dw and SSDE).It is interesting to analyze the results of Dw and SSDE based on the old and the new algorithms for images consisting of more than one substantial object are tabulated in Table w with the old and new algorithms is significant, that is, about 40%. It clearly shows that the calculation of Dw based on the old algorithm for images consisting of more than one substantial object gives lower Dw values than those from the new algorithm. This leads to the SSDE values calculated by the old algorithm being greater than those calculated by the new algorithm.The differences of the results of D4w. The auto contouring must be able to involve all parts of the patient's body and minimize the contribution of irrelevant objects such as the CT table. Setting the patient boundaries based on the largest object area . However, the position of the table can vary based on many factors, such as field of view and size of the patient. Investigation of the limit of y‐position for more clinical datasets from different scanners and various field of views will be useful.The CT table may be among the six largest objects in the field of view. Previous studies have reported the impact of the CT table on the CT dose.In addition, in the current study, the threshold value for segmentation is adopted from a previous study w and SSDE for all images. If the earlier algorithm was used for measuring the Dw and SSDE from images with more than one substantial object, the results can be corrected using the relationships in Figures R2 > 0.9). The new modified algorithm is a fast and simple alternative way of providing accurate values of patient diameter and estimated dose.Detection of the six largest objects and removal of the patient table results in a more accurate measure of D5w and SSDE estimates is presented in this study. The algorithm employs detection of the six largest objects within an image and deletion of the CT table. The algorithm gives the same results for Dw and SSDE as the earlier algorithm when images consist of only one substantial object. However, with images consisting of more than one substantial object, the new algorithm is able to detect all parts of a patient within the image and subsequently gives significantly higher Dw values and lower SSDE values compared to the earlier algorithm. The percentage differences of the Dw values between the new and old algorithms are 9.5%, 15.4%, and 39.6% for the chest region with one arm placed down, both arms placed down, and images with two separated legs, respectively. The new algorithm represents a more general approach for accurate Dw and SSDE estimation.An improved automated method to calculate DDr. Choirul Anam and Dr. Freddy Haryanto are IndoseCT developers. This software is not commercially available. It can be made available on request for research purposes. Rest of the authors have no conflicts of interest to disclose.w and SSDE on patient images based on the developed software. WKS, CA, and GD wrote the paper. All authors approved the final version of the paper.CA and FH conceived the idea. CA and FRM developed the software. WKD, HS, PT, and CA calculated the D


Proximal hypospadias defects represent the most challenging aspect of maintaining blood supply to the flap, which eventually leads to a high rate of complications. We modified a sealed Y‐shaped penis foreskin vascular protection technique, which can repair the urethra in a single stage. The inner plate of the foreskin was cut along the coronal sulcus, and both sides of the urethral plate were cut as deep as Buck's fascia. The "Y"‐shaped foreskin flaps on both sides of the mouth that are continuous with the urethral plate were sutured to form a new urethral skin tube. The urethral skin tube was turned to the ventral side, and the foreskin was reshaped and sutured. A total of 89 children had their urinary catheters removed 4 weeks after the operation. All children were evaluated at least once a year for 3 consecutive years. There were 11 patients with urine leakage that occurred after the operation. These children, diagnosed with urine leakage, underwent successful repair after the leakage occurred. There were no urethral strictures after the operation. The one‐time success rate of this operation was 87.6% (78/89), and the incidence of urethral fistula was 12.6% (11/89). The results showed that sealed Y‐shaped penis foreskin vascular protection surgery was safer and had a higher operation rate than the traditional hypospadias repair technique. Modifying Koyanagi repair by our improved Koyanagi hypospadias repair is an excellent technique with relatively low complication rates. We designed a closed Y‐shaped flap repair. In this operation, we used the Y‐shaped flap to synthesize a new urethra. The unique incision to protect the vascular system of the Y‐shaped flap enables one‐time repair of hypospadias with complete repair of penile curvature with low complication rate. The vulvar area was washed with running water 12 h before surgery. Fasting was performed 6–8 h preoperatively, and cefuroxime was administered 30 minutes preoperatively. With the patient in the supine position, 0.05% complexed iodine was used to disinfect the skin, and 0.05% complexed iodine was used to disinfect the foreskin and urethral opening. Next, an F8 catheter was smoothly inserted into the bladder and left in, and 0.05% complexed iodine was again used to disinfect the foreskin. No. 1 silk thread was used to suture the dorsal side of the glans as a traction line.Circumcise the foreskin and separate the foreskin from the corpus cavernosum. A laser knife is used to circumcise the circumcision (cut off the deep fascia layer) along the dotted line, leaving the foreskin on the glans side 3–5 mm Figure . The forCreate a 5‐mm‐wide, sealed, Y‐shaped skin flap. The skin and superficial fascia layer are incised along the incision 5 mm at the upper end of the foreskin to retain the deep fascia layer and the vascular layer above it, thus forming the upper part of the closed Y. At the same time, the skin and superficial fascia layer are incised at 5 mm on the left and right sides of the skin along the midline of the urethral outlet at 5 mm from the lower end of the urethral orifice to form the lower part of the Y‐line flap. This part is critical to the success of the operation. We need to carefully protect the vascular layer on the deep fascia layer from being damaged so that the blood supply of the flap used in the new urethra will not be necrotic Figure .Separate the dorsal foreskin along the superficial tendon layer: Use a scalpel to separate the foreskin into a superficial tendon layer and a deep tendon layer. Carefully use a scalpel to separate along the inner side of the superficial tendon layer without destroying the blood vessel layer. The skin will be divided into two layers: one layer is composed of a blood vessel layer and deep fascia, and the other layer is composed of skin and superficial fascia Figure .Create a new urethra. The inner side of the Y‐shaped flap is sutured to form the inner side of the urethra Figure , and theCorrect the position of the new urethra and penis: Create a hole in the middle of the deep tendon layer on the back Figure , then paFix the urethral opening: Create a hole in the center of the glans on the ventral side of the penis, and cut off the top of the glans as a new urethral exit Figure . The newFix the new urethra: Fix the urethra with absorbable sutures along the middle of the abdomen of the penis Figure Suture the coronary sulcus:Make a hole in the middle of the skin and superficial fascia layer separated in step 3 and then suture the urethral opening and the glans incision Figure ; next, pSuture the skin of the penis: Suture the remaining foreskin to form a complete skin Figure .The surgical steps are as follows:The penile wound was wrapped with petroleum jelly gauze, the scrotal wound was pressed with petroleum jelly, cefuroxime was used for 5 days after the operation, and the petroleum jelly‐impregnated gauze was removed 1 week after the operation. The urinary catheter was removed 4 weeks after the operation, and urination was observed.Tips: (1) When constructing the Y‐shaped flap, only the superficial fascia layer can be cut, not the blood vessel layer.(2) When separating the back foreskin, only the superficial fascia layer should be separated as much as possible, and the blood vessels between the superficial fascia layer and the deep fascia layer should not be injured. Not destroying blood vessels is the key to reducing complications and the success of this operation.3From 2014 to 2017, 29 cases of perineal hypospadias and 60 cases of scrotal hypospadias were performed at our institutions. The ages of the patients ranged from 1 to 10 years (mean = 4 years). All children had their urinary catheters removed 4 weeks after the operation. All children had their urinary catheters removed at 1, 3, and 6 months and then were evaluated at least once a year for 3 consecutive years. Nine cases of urine leakage occurred within 3 days after the operation. The leakage orifices were all located at the junction of the penis ventral penis and scrotum. The diameter of the leakage orifice was approximately 2–5 mm. One patient had urine leakage 3 months after the operation, and 1 patient had urine leakage 5 months after the operation. Urine leakage occurred in the first month, and the leakage opening was located in the middle of the ventral side of the penis, with a diameter of approximately 1 mm. It is believed that the needle eye caused the leakage of urine after the absorbable thread was absorbed. One case of urine leakage was caused by skin infection 1 year after the operation. All the children with urine leakage underwent repair after the leakage occurred 1 year later, and the leakage was successfully repaired at one time. There was no urethral stricture after the operation. The one‐time success rate of this operation was 87.6% (78/89), and the incidence of urethral fistula was 12.6%  The material of the new urethral flap is sufficient. The new urethroplasty flap used in this operation is continuous from the urethral opening to the dorsal foreskin inner plate. The length of the flap is exactly the distance from the ectopic urethral orifice to the tip of the penis. The length of the flap should not be measured. (2) The original natural anatomical structure is preserved. It is currently believed that the urethral plate shows the natural urethroplastic structure of hypospadias, and the inner plate of the foreskin and the urethra come from the same germ layer during embryonic development. Utilizing the inner foreskin and urethral plates on both sides of the urethra orifice as the new urethroplasty material is most in line with the natural physiological anatomy of hypospadias; at the same time, the original axial blood supply of the entire skin flap of the new urethra is almost unchanged, which is conducive to wound healing and surgery. In this study, after the complications were repaired again, the success rate of the reoperation in this group was higher. (3) Bilateral scrotum sutures are used to eliminate scrotal division and translocation. (4) There is no anastomosis in the new urethra. The joint of the original urethral orifice of the operation is formed by a rolled tube urethral plate. There is no need for circular anastomosis, which can significantly reduce complications such as anastomotic stenosis. (5) The success rate is high. Preserving the blood supply of the distal foreskin so that the flaps will not be too long and receive a poor blood supply, especially with the scrotum or the testicular tunica on both sides of the scrotum covering the ventral side of the formed urethra to increase the level of tissue and prevent suture overlap, significantly reduces the occurrence of complications such as urinary fistula. (6) The surgical technique is not demanding, is easy to master, yields a good postoperative appearance, and facilitates smooth urination. In this group, 93.3% of the external genitalia were satisfactory in appearance, and the obstructive urinary flow curve was only 12.5%. Disadvantages: (1) There is a suture opening on the dorsal and ventral sides of the urethra, which needs to be sutured carefully, and the thread knot in the urethra, when sutured on the dorsal side, should not be too long. The author generally keeps 1–1.5 mm, and the absorption time of the absorbable thread should be within 1 month, otherwise the thread knot is too long and urination is not smooth; moreover, some of the thread‐knot urinary scale will condense, and urethral stones will appear. (2) The operation is simple, but it requires experience.The indications for surgery are applicable to any severe proximal hypospadias, especially hypospadias accompanied by scrotal division and penile–scrotal translocation.Intraoperative precautions: (1) The urethroplasty flap is a fascial flap pedicled at the urethral opening, and the blood supply of the flap must be protected; (2) When the new urethral flap is formed in a tubular shape, the thread knots are outside, with no tension, and the shape is formed. There is no leakage of urine afterward. (3) The anastomosis between the new urethral skin tube and the head of the penis is close to the ventral side, the new urethral opening will no longer be on the same side, and there will be no urethral stenosis. In short, stage I urethroplasty with a continuous Y skin flap of the penis and scrotum makes full use of and rarely destroys the natural anatomical and physiological structure of the original hypospadias. Thus, it is an ideal surgical method for proximal hypospadias.The authors declare no competing financial interests.JCZ and XWP designed the experiments JCZ, YFC, YLW, YL, and TQH analyzed the data. XWP and JCZ wrote the manuscript. JCZ, YFC, YL, TQH, YLW, YEZ, and XWP revised the manuscript. All authors approved the final version of the manuscript.The Ethics Committee of Changsha Maternal and Child Health Hospital approved this study.Written consent has been obtained from the father/mother of the patient prior to the submission of this case report.Table S1Click here for additional data file.


The frequency and intensity of extreme events especially heat waves (HW) are growing all around the world which ultimately poses a serious threat to the health of individuals. To quantify the effects of extreme temperature, appropriate information, and the importance of HW and heat index (HI) are carefully discussed for different parts of the world. Varied definitions of the HW and HI formula proposed and used by different countries are carried out systematically continent-wise. Different studies highlighted the number of definitions of HW; however, mostly used Steadman’s formulae, which was developed in the late 1970s, for the calculation of HI that uses surface air temperature and relative humidity as climatic fields. Since then, dramatic changes in climatic conditions have been observed as evident from the ERA5 datasets which need to be addressed; likewise, the definition of HW, which is modified by the researchers as per the geographic conditions. It is evident from the ERA5 data that the temperature has increased by 1–2 °C as compared to the 1980s. There is a threefold increase in the number of heatwave days over most of the continents in the last 40 years. This study will help the researcher community to understand the importance of HW and HI. Furthermore, it opens the scope to develop an equation based on the present scenario keeping in mind the basics of an index as considered by Steadman. The occurrence and strength of extreme heat events are increasing all around the world which has direct and indirect impacts on the health of living beings and the ecological system. Extreme heat events are physical hazards that cause health issues in eerie  is not limited to the health of human beings  which accounts for humidity along with the actual temperature , although it is commonly defined as limited successive days with high temperatures above a certain threshold based on community and physiology  plays a vital role to calculate the effects of extreme heat events, because it is not only the heat that poses an effect. It is also the RH that is used to determine how hot and humid it feels based on the combined effect of temperature and humidity. This combined effect is represented by the mathematical term called HI. Basically, HI measures how hot it feels actually when RH is considered with air temperature. HI is one of the methods which can be used to access the potential risk of an extreme event like HW. As the environmental conditions of all the countries in the world are not the same, different countries use different definitions to define the HW based on different metrological parameters range. Additionally, they calculated HI for their area using the Steadman Scheme. This is developed by using multiple regressions such as the Poisson Regression analysis technique on the meteorological data Steadman . This fowhere HI: Heat Index in °F; RH: Relative Humidity in %, T: Ambient Dry Bulb Temperature in °F.This formula is used by different researchers around different parts of the world to access the effects of HW on human beings. Based on HI, the heat index calculator or heat index chart provide different zones like caution, extreme caution, danger, and the extreme danger zone Table .Table 2RBy understanding these zones, necessary steps will be taken to minimize the effects of HW. It is interesting to find in the coming section that most countries use Eq.  for the Weather variables were used in this study as direct input to the HI equation. Data were obtained from a public dataset, ERA5  found matches to the average temperature of India during the summer season. The study of HI in 2–50 °C f. The meteorological data of Taiwan was used to calculate the value of HI from 1994 to 2008. It contained the least daily mean values of Temperature and RH. When the meteorological data was substituted in Eq. , and temperature indicator , etc. as shown in Fig. The main purpose of this study focused on the definition of HW and HI for different countries which is characterized by the data accumulated from different sources. Overall, findings indicate that (i) at one region, HW may be termed for consecutive several days and at the other, even for 1 day. (ii) HI is consistently dependent on few parameters like air temperature and relative humidity, (iii) Steadman’s equation is still used for quantification of the effect of HW. This study helps to understand an outlook for developing/modification of the existing HI equation for a country like India where the diverse temperature is observed by either developing an assortment of such equations for the entire country with special parameters included to result into the different equations for different regions in From Fig. The most discussed topic of present scenario in which relation of COVID1-9 cases with meteorological parameters are discussed  i.e., “A period during which the daily maximum temperature exceeds for more than five consecutive days by the maximum normal temperature of 9°F (5 °C)” but most of the countries have modified the definition as per their regional metrological data. Quantitatively, HI confirmed that the temperature experienced during the summer months with high humidity values is more in comparison to the other months. Generally, the different researchers used the Steadman equation for the calculation of HI. Accordingly, different countries developed or suggested their own precautionary steps to minimize the effects of HW caused by heatstroke and heat exhaustion like living in air-conditioned homes, etc. A warning system based on HI can be designed to study the effect of extreme events based on combined observation of high daytime temperatures, warm nighttime temperature, high humidity, and light winds for several successive days.It is inferred from different studies that there is no exact definition of HW, as these vary with respective regions. Different studies implied that it is difficult to develop a generic formula for HW for all the countries worldwide. It is evident from the ERA5 data that the temperature has increased by 1–2 °C and number of heatwaves has been increased three times and in most of the continents in the last 40 years. It is observed from HI calculation that both relative humidity and temperature are the main components for the equation and in most cases; Steadman’s equation is used to quantify the effect of an extreme event like HW. Hence, to develop/modify an equation of HI depends upon a diverse range of meteorological parameters like temperature, RH, etc. Therefore, an enormous study of such parameters is required.The development of a generic index for HW requires special correction parameters (or factors) or a value or a relation with respect to region. For this purpose, study on a mass level is required and countries need to be selected region-wise and based on meteorological variations so that every perspective of diverse variation in weather can be included. This study summarizes the basic ideas about the HW and HI so that this knowledge can provide a platform to develop or modify the existing definitions and formulas so that necessary steps can be taken to minimize the hazardous effect of extreme events like HW. As there are several definitions for HW, but for HI, Steadman’s equation is still used as a generic equation. There is a need to develop separate HI equations as per different geographical and climatic conditions. Therefore, the development/modification of the appropriate HI equation is needed for an hour and acts as future scope for this paper. The mathematical aspect of the HI equation can be handled by the mathematical expert with the joint effort of environmentalists for the development of HI.


An enduring question in biology is how a single cell—the fertilized egg, or zygote—can generate the incredible complexity and variety of tissues of a complete organism. Much of this process depends on the establishment and maintenance of polarity, both in single cells and within a population of cells. Simply put, how does a cell or an embryo know its front from its back?Caenorhabditis elegans embryo depends on a “polarization center” that is formed by the descendants of one of the two initial blastomeres—the cells that result from the first division of the zygote. The posterior blastomere gives rise to a group of cells that are responsible for the anterior–posterior organization of most of the embryo.In a new study, Marcus Bischoff and Ralf Schnabel show that the polarization of the C. elegans embryo could influence the orientation of their anterior neighbors and that this influence depends on the signaling molecule Wnt. Bischoff and Schnabel used experiments in which they cultured different combinations of blastomeres to investigate the extent and mechanisms of this effect.It has been clear for some time that posterior cells in the early When the anterior blastomere (AB) is isolated from the posterior (P1) cell at the two-cell stage, the dividing cells form a spherical shape. Each cell divides along a cleavage axis; in cultures of isolated anterior blastomeres, the direction of cleavage rotates by about 90 ° after each division, and the divisions fall along an axis that deviates from the anterior–posterior axis by about 63 °. The embryonic fragments formed by this process are spherical. In normal embryos, the descendants of AB cleave along an axis that falls about 45 ° away from the anterior–posterior axis, and the embryos are elongated.When the authors added a P2 blastomere—one of the descendants of P1—to the isolated anterior cells, it caused a shift in the orientation of division toward the P2 cell, even in those anterior blastomeres that didn’t touch the added cell. It also caused the spherical embryonic fragments to elongate, and the elongation and shift in orientation of division were highly correlated. Further analysis showed that the elongation was a direct result of the reorientation of the cell divisions by P2.To investigate the role of Wnt signaling in this process, Bischoff and Schnabel carried out similar experiments using blastomeres from embryos with mutations in the gene that encodes the MOM-2/Wnt signaling molecule. When the added P2 cell lacked Wnt, the anterior cells failed to elongate or to orient toward P2. The same effect was seen when the AB-derived blastomeres lacked the Wnt receptor MOM-5/Frizzled, showing that P2 induces reorientation and elongation by producing Wnt, which stimulates Frizzled receptors on AB-derived cells.The properties of this polarizing center were tested using various arrangements of blastomeres. When two AB blastomeres were added to opposite sides of one P2 cell, both oriented toward P2, showing that it signals in all directions. P2 can orient the granddaughter cells of AB as well as the daughter cells, and the daughter cells of P2 have the same effect. Furthermore, the polarizing signal can reach cells that do not touch P2. If the intervening AB descendants lack the ability to produce Wnt, however, the signal does not reach the cells that are not touching P2, showing that the polarizing Wnt signal is transduced from cell to cell by a relay mechanism—each cell, when stimulated by Wnt, in turn produces Wnt to stimulate its neighbors. To confirm this finding, the authors showed that AB cells that had been oriented by a P2 cell could orient other AB descendants even when the P2 blastomere had been removed. This is the first demonstration that cell polarity is passed on from cell to cell by a so-called relay mechanism.Drosophila and vertebrates, where it is also necessary to organize polarity in fields of cells. Further work will also be needed to investigate how this mechanism is related to the initial anterior–posterior organization of the zygote by par genes, which give rise to the initial distinction between anterior and posterior cells.This simple mechanism could have parallels in other organisms, such as


Many biological specimens, such as living cells and their intracellular components, often exhibit very little amplitude contrast, making it difficult for conventional bright field microscopes to distinguish them from their surroundings. To overcome this problem phase contrast techniques such as Zernike, Normarsky and dark-field microscopies have been developed to improve specimen visibility without chemically or physically altering them by the process of staining. These techniques have proven to be invaluable tools for studying living cells and furthering scientific understanding of fundamental cellular processes such as mitosis. However a drawback of these techniques is that direct quantitative phase imaging is not possible. Quantitative phase imaging is important because it enables determination of either the refractive index or optical thickness variations from the measured optical path length with sub-wavelength accuracy.Digital holography is an emergent phase contrast technique that offers an excellent approach in obtaining both qualitative and quantitative phase information from the hologram. A CCD camera is used to record a hologram onto a computer and numerical methods are subsequently applied to reconstruct the hologram to enable direct access to both phase and amplitude information. Another attractive feature of digital holography is the ability to focus on multiple focal planes from a single hologram, emulating the focusing control of a conventional microscope.A modified Mach-Zender off-axis setup in transmission is used to record and reconstruct a number of holographic amplitude and phase images of cellular and sub-cellular features.Both cellular and sub-cellular features are imaged with sub-micron, diffraction-limited resolution. Movies of holographic amplitude and phase images of living microbes and cells are created from a series of holograms and reconstructed with numerically adjustable focus, so that the moving object can be accurately tracked with a reconstruction rate of 300ms for each hologram. The holographic movies show paramecium swimming among other microbes as well as displaying some of their intracellular processes. A time lapse movie is also shown for fibroblast cells in the process of migration.Digital holography and movies of digital holography are seen to be useful new tools for visualization of dynamic processes in biological microscopy. Phase imaging digital holography is a promising technique in terms of the lack of coherent noise and the precision with which the optical thickness of a sample can be profiled, which can lead to images with an axial resolution of a few nanometres. Dennis Gabor invented holography in 1948 while attempting to improve upon the resolution of electron microscopy. Although at the time this new invention was a significant advance, the requirement of a coherent light source precluded its practical use. The invention of the laser and the introduction of off-axis holography provided the critical elements to make holography a practical and powerful tool for large areas of applications from metrology, data storage, optical processing, device fabrication, and even fine arts. While the use of lasers and the off-axis technique made holographic recording and reconstruction a valuable and practical technique, the conventional process of performing holography using photographic plates is time-consuming and cumbersome. Real time processing of the hologram is not feasible unless one uses photorefractives and other nonlinear optical materials.Recently, the field of holography has been undergoing another paradigm shift by electronic image capture using CCD array cameras and digital processing of the holographic images ,2. With The applications of digital holography to microscopy are particularly advantageous because in conventional microscopy the very narrow depth of focus requires mechanical scanning of the focal plane, whereas a single hologram records the information of a three-dimensional object space. These advantages have been noted since early in the development of digital holography and applied to imaging analysis of microstructures,13 and bIn this paper we present the results of our recent experiments on digital holography for biological microscopy. Cellular and sub-cellular features are imaged with diffraction limited resolution of 0.5 μm and the optical thickness profile is determined from the analysis of the measured phase information. As examples to determine the resolution of the optical system as well as illustrate the unique properties of digital holography we examined a standard optics test object, the USAF 1951 resolution target and a diverse range of biological objects such as red blood cells, mouse embryo fibroblast cells, and paramecium. To show the adaptability of digital holography in dynamic imaging of living specimens and cells, movies of holographic amplitude and phase images were created from a series of holograms and reconstructed with numerically adjustable focus.2 active area, with 8-bit gray scale output, and the electronic shutter speed of the of the camera is usually set at 1/30 sec. A digital delay generator (Stanford Research DG535) triggers both the laser and the camera at a repetition rate of 20 Hz. An IEEE1394 cable connects the camera to the desktop computer, which processes the acquired images and calculates the holographic diffraction using a number of programs based on LabVIEW® and MatLab®.The setup for the recording of digital holograms is depicted in Fig. H|2 is recorded by the camera and consists of;The hologram which is created by the interference between the object and reference fields, |H|2 = |O|2 + |R|2 + O*R + OR*     (1)|O and R are the object and reference fields, respectively. Separate exposures and subtraction of the zero-order background |O|2 and |R|2 helps to reduce the zero order term significantly. Once |O|2 and |R|2 are removed the holographic twin image O*R + OR* terms remain. A slight angle is introduced between the object and the reference beams by tilting the beam splitter BS2 in order to separate the conjugate twin images, O*R + OR* for an off-axis hologram. The object is moved back a distance z from the focal plane of the CCD to create the out of focus image of the object.where Once the hologram has been captured the optically diffracted field is numerically propagated by use of reconstruction algorithms to calculate the optically diffracted field -22. HereE is found byIn the convolution process  the recoand the convolution isE0  ⊕ SH      (3)= F-1[F (E0)·F (SH)]= SH is the Huygens PSFwhere The whole process requires three Fourier transforms, which are carried out using the FFT algorithm. The pixel sizes of the images reconstructed by the convolution approach are equal to that of the hologram. In order to achieve as high a lateral resolution as possible, one keeps the object-hologram distance as short as possible, but the discrete Fourier transform necessitates a minimum distance such that:ax is the frame size and nx is the number of pixels in ax. At too close a distance, the spatial frequency of the hologram is too low and aliasing occurs. Normally the object is placed just outside this minimum distance.where The Fresnel transformation is the most commonly used method in holographic reconstruction. The approximation of spherical Huygens wavelet by a parabolic surface allows the calculation of the diffraction integral using a single Fourier transform. The PSF can be simplified by the Fresnel approximation as:and the reconstructed wave-field is:x of the reconstructed images determined directly from the Fresnel diffraction formula will vary as a function of the reconstruction distance z as:The pixel resolution ΔN is the number of pixels and Δx0 is the pixel width of the CCD camera. As with the Huygens convolution method there is a minimum z distance requirement set by Equation 5.where E0 is the wave-field at plane z = 0, the angular spectrum A at this plane is obtained by taking the Fourier transform:The angular spectrum method of reconstruction has the significant advantage that it has no minimum reconstruction distance requirement as is this case for the Fresnel and convolution methods. If E0 = ∬E0 exp[-i(kxx0 + kyy0)]dx0dy0     (9)kx and ky are corresponding spatial frequencies of x and y. Fourier-domain filtering can be applied to the spectrum to block unwanted spectral terms in the hologram and select a region of interest corresponding only to the object spectrum. Subsequently the wave-field E0 can be rewritten as the inverse Fourier transform of its angular spectrum,where E0 = ∬A exp[i(kxx0 + kyy0)]dkxdky     (10)A is calculated from A asThe new angular spectrum at plane z, A = A exp[ikzz]     (11)where z axis is found byThe reconstructed complex wave-field of any plane perpendicular to the propagating E = ∬A exp[i(kxx + kyy)]dkxdky     (12)Two Fourier transforms are needed for the calculation in comparison to the one needed by the Fresnel transform. However once the field is known at any one plane, only one additional Fourier transform is needed to calculate the field at different values of z. This method allows frequency-domain spectrum filtering to be applied, which for example can be used to block or remove the disturbance of the zero order and twin image components. A signficant advantage of the angular spectrum is that there is no minimum z reconstruction distance requirement.Here we present some examples of quantitative phase images obtained from digital holography experiments that examine the lateral resolution of the optical system and demonstrate the quantitative and qualitative capabilities of digital holography for microscopy. The holograms are recorded at a distance z from the focal plane of the CCD camera and thus are not recorded in focus. In each case the reconstruction distance for best focus is determined by both human observation and a contrast measurement algorithm to check for accuracy. The reconstruction here is performed by application of the angular spectrum algorithm which has no set minimum reconstruction distance. Figure ξ = 0.61λ/N.A ~ 0.81 μm.     (13)ΔThe resolution target used is a positive mask with opaque chrome film on a transparent glass plate. The physical thickness of film is given byd = λ(Δφ/2π)/(n - n0)     (13)λ is the wavelength, Δφ is the phase step, and n-n0 is the index difference between the film and air. From the measured optical path length change it is possible to determine either the optical thickness or refractive index provided knowledge of one or the other is known.where π ambiguities in the wrapped phase image and apply a linear phase offset correction to the image in Fig. Quantitative phase imaging is particularly effective in digital holography – one only needs to plot the phase of the calculated complex optical field, as shown in Fig. d as seen from Equation 13. From the analysis of the phase map Fig. In Figure 2 and calculated in the range of z = 50 ~ 250 μm in steps of 10 μm. The sequence of images is then composed into an AVI movie  into the food vacuoles. A notable phenomenon in the phase movie is the shrinking of the contractile vacuole as it pumps out water. . shows thWe have presented experimental results that demonstrate the capabilities of digital holography for biological microscopy. A series of holograms are recorded and the images are reconstructed with numerically adjustable focus so that the moving objects can be accurately tracked. The holographic reconstruction is carried out by the Angular Spectrum method in an off-axis configuration so that the twin images and zero-order background can be subtracted out using frequency domain analysis. The lateral and longitudinal resolutions obtained are consistent with diffraction limited imaging. Digital holography and movies of digital holography are seen to be a useful new tool for biological microscopy, with noteworthy advantages over traditional microscopic techniques for biological imaging.The speckle noise of a coherent imaging system is a significant issue for biological microscopy where there is often a large range of structural scales. For example, in the presented holographic amplitude images of paramecium the intracellular regions contain various assortments of unresolved particles, which may be food particles or other organelles. These degrade the quality of images to various degrees. On the other hand, one also notices that the phase images tend to suffer from the coherent noise to a significantly lesser degree compared to the amplitude images. We note that in this study we have presented the images with minimal post-processing, the goal being the demonstration of digital holography processes. Other than the overall brightness and contrast adjustments, we have not applied any of the numerous image enhancing techniques that are available, which cPhase imaging digital holography is particularly promising in terms of the lack of coherent noise and the precision with which the optical thickness can be profiled, which can lead to images of high axial resolution of a few nanometres. However the phase images are often required to be unwrapped in order to simplify their interpretation. Of particular significance is the 2π ambiguity problem in phase-imaging holography. A conventional approach is to apply one of many phase-unwrapping algorithms as we have done in this paper, but often these require both substantial user intervention and strict requirements on the level of phase noise and the phase discontinuity. They also have difficulty in correctly interpreting the phase images of rough and irregular objects. We are currently investigating multi-wavelength phase-imaging digital holography for geneNumerical focusing of paramecia from a single hologram.Click here for fileHologram movie of a paramecium and euglena.Click here for fileAmplitude movie of a paramecium and euglena.Click here for filePhase movie of a paramecium and euglenaClick here for fileHigher magnification hologram movie of a parameciumClick here for fileHigher magnification amplitude movie of a parameciumClick here for fileHigher magnification phase wrapped movie of a parameciumClick here for fileHigher magnification phase unwrapped movie of a parameciumClick here for fileHologram movie of mouse-embryo fibroblast cells in the process of migrationClick here for fileAmplitude movie of mouse-embryo fibroblast cells in the process of migrationClick here for filePhase movie of mouse-embryo fibroblast cells in the process of migrationClick here for filePhase unwrapped movie of mouse-embryo fibroblast cells in the process of migrationClick here for fileThree-dimensional optical thickness profile phase movie of mouse-embryo fibroblast cells in the process of migrationClick here for file


Carbohydrates play a critical role in human diseases and their potential utility as biomarkers for pathological conditions is a major driver for characterization of the glycome. However, the additional complexity of glycans compared to proteins and nucleic acids has slowed the advancement of glycomics in comparison to genomics and proteomics. The branched nature of carbohydrates, the great diversity of their constituents and the numerous alternative symbolic notations, make the input and display of glycans not as straightforward as for example the amino-acid sequence of a protein. Every glycoinformatic tool providing a user interface would benefit from a fast, intuitive, appealing mechanism for input and output of glycan structures in a computer readable format.A software tool for building and displaying glycan structures using a chosen symbolic notation is described here. The "GlycanBuilder" uses an automatic rendering algorithm to draw the saccharide symbols and to place them on the drawing board. The information about the symbolic notation is derived from a configurable graphical model as a set of rules governing the aspect and placement of residues and linkages. The algorithm is able to represent a structure using only few traversals of the tree and is inherently fast. The tool uses an XML format for import and export of encoded structures.The rendering algorithm described here is able to produce high-quality representations of glycan structures in a chosen symbolic notation. The automated rendering process enables the "GlycanBuilder" to be used both as a user-independent component for displaying glycans and as an easy-to-use drawing tool. The "GlycanBuilder" can be integrated in web pages as a Java applet for the visual editing of glycans. The same component is available as a web service to render an encoded structure into a graphical format. Finally, the "GlycanBuilder" can be integrated into other applications to create intuitive and appealing user interfaces: an example is the "GlycoWorkbench", a software tool for assisted annotation of glycan mass spectra. The "GlycanBuilder" represent a flexible, reliable and efficient solution to the problem of input and output of glycan structures in any glycomic tool or database. Chemically, monosaccharides are aldehydes or ketones with two or more hydroxyl groups. They can exist as linear molecules but more usually they cyclize to form ring structures. Complex carbohydrates  are formed by combinations of monosaccharides covalently linked by glycosidic bonds: two types of glycosidic bonds can be formed, α and β, depending on the orientation of the anomeric centres of the monosaccharides involved. Each hydroxyl group of a monosaccharide constitutes a possible point of formation for a glycosidic bond. Therefore, glycans can have very complex structures with many branching points. The monosaccharides are classified according to the number of carbon atoms they contain, the position of the anomeric centre, and its chiral handedness. Glycans are classified accordingly to the number of monosaccharide units they contain: large polysaccharides  containing many thousand repeating units whereas smaller oligosaccharides can contain between two and ten monosaccharides.Carbohydrates are the most abundant type of biological molecules. The smallest structural unit of a carbohydrate is called a glycoconjugates. Apart from their well known use in energy storage and expenditure, the roles of carbohydrates in living organisms are varied and fundamental. Glycans can have structural and modulatory functions by themselves or can modulate the function of the molecules to which they are attached by the specific recognition of the glycan structure by carbohydrate-binding proteins (lectins). Glycans regulate both the folding and degradation of proteins. Moreover, since the outer cell membrane is covered by carbohydrates, they mediate interactions with other cells of the same organism or other pathogenic organisms such as viruses, bacteria and multi-cellular parasites. The critical role of glycans in diseases and their utility as biomarkers for pathological conditions have now been widely recognized [Carbohydrates can be found as homo-polymers but are usually attached to other biomolecules such as lipids or proteins to form complexes named cognized  and haveThe additional complexity of glycan structures compared to proteins and nucleic acids has slowed the advancement of glycomics. The branched structure of glycans means that most of the bioinformatic tools developed for analysis of linear proteins and nucleic acids cannot be simply adapted to the sequencing of glycan structures. Moreover, no extensive database of known glycan structures and related primary data is currently available, although various initiatives in this direction are currently in place. Therefore, the current situation sees a critical lack of software tools for almost every aspect of glycomic research .The branched non-sequential nature of glycan structures and the great diversity of their constituents imply that the input of a structure into a computer readable format is not as straightforward as writing a sequence of characters for representing the amino acid sequence of a protein chain. Additionally, numerous alternative symbolic notations are commonly adopted to represent glycan structures in publications. Every glycoinformatic software tool providing a user interface would benefit from a fast, intuitive, graphically appealing mechanism for input and output of glycan structures. For example, database application would profit from an easy to use interface for performing structure searches and displaying search results.A user friendly input/output tool for glycan structures should satisfy three fundamental requirements: provide an intuitive interface to build structures with minimal user interaction, allow the encoding in a standard format also readable with other software tools and create conventional and appealing graphical representations of glycans. These requirements are usually diverging. A common practice is to employ graphical editors for drawing the structures to be published in research papers. Graphical editors give the user the highest freedom in creating the graphical representation. However, it is realistically impossible for a software tool to extract useful information about the glycan structure from the resulting geometrical representation. Moreover, graphical editors require a large amount of user interaction for conveying the desired result. Some input tools that have been developed for searching structure databases mimic the operations of a graphical editor. The symbols for saccharides and linkages are usually predetermined but the user can freely position the saccharides on the drawing board. Two editors follow this approach: KCAM ,4 and DrThe EUROCarbDB design studies  aim to cThe representation of glycan structures in a computer readable format is an essential prerequisite for the exchange of information between databases and for the use of stored data by bioinformatic tools. The string encoding format for glycan structures proposed by IUPAC-IUBMB  was explA critical problem with the previous formats is exemplified by the representation of monosaccharide components. The convention used for naming monosaccharide monomers results in multiple names for the same basic chemical configuration. This ambiguity is not easily addressed by software tools. One of the efforts taken by the EUROCarbDB project has been the development of a controlled vocabulary for monosaccharides centred on a database of monosaccharides units. Each unit is represented as an unmodified saccharide base type (e.g. glucose) plus a list of substituents and modifications. EUROCarbDB has proposed a glycan encoding format based on this monosaccharide representation, Glyco-CT . Glyco-CThe "GlycanBuilder" uses the Glyco-CT format for the import/export of glycan structures, while suitable libraries are being developed by EUROCarbDB for translating Glyco-CT into other common encoding formats and vice versa.Symbolic representations of glycans consist of a series of geometric symbols that represent monosaccharide units connected by lines to indicate glycosidic linkages. Symbolic notations are widely used in publications, especially for representing mammalian structures, because they enable a very compact way of describing complex glycans, useful when a large series of structures needs to be displayed (i.e. the profile of glycans expressed by a cell population). Moreover, symbolic representations are much easier for humans to recognize and allow a rapid comparison of structures when determining differential expressions in biological contexts. Unfortunately, no standard notation exists and many different types of symbols and conventions can be found in the literature.The Consortium for Functional Glycomics (CFG) has issued a recommendation for the symbolic representation of the glycan structures present in mammalian organisms. This notation is used by all Consortium databases and is being employed in the second edition of the book "Essentials of Glycobiology" . Due to A second proposal for representing linkage information, originally suggested in , has beeThe "GlycanBuilder" is based on an automatic rendering algorithm able to transform a computer encoded glycan structure into a pictorial representation determined by the chosen symbolic notation. The encoded structure is first parsed and transformed into a data object that stores all the information about the glycan . The data object is then rendered into the desired output media using a graphical model representing the symbolic notation of choice. The rendering process is completely automatic: the spatial placement and aspect of residues depends only on the symbolic notation. This means the "GlycanBuilder" can be used as an automated component for displaying glycan structures. Additionally, when the "GlycanBuilder" is employed as an interactive drawing tool, this feature makes the user free from any responsibility regarding the drawing and creates faster and easier-to-use interfaces.The internal object used to store and manage information about glycans in the "GlycanBuilder" is a tree structure whose nodes are the glycan residues and whose edges are their linkages. Each node represents a drawable object, an enlarged concept of structural constituent comprising: saccharides, reducing-end specificators and markers, substituents and saccharide modifications. Reducing-end specificators are used to identify possible modifications at the reducing-end terminal (or no modifications). A special node is also defined for collecting glycan terminals with unspecified linkages, a common way of describing structures with incomplete topological information . Each condition matches a certain parent, linkage or child attribute: residue type, linkage position, anomeric state, position of the anomeric carbon or residue class . The attributes of a rule are: a set of possible positions for this residue, a flag identifying if the residue is on a border region, a flag identifying if the orientation of the child is the same as the parent or should rotate accordingly to the position angle, a flag identifying if the position is "sticky". If a position is "sticky", all the subsequent children are placed in position 0: in this way it is possible to force sub-trees in regions with a change in orientation to be drawn as a sequences, thus avoiding the creation of spiralling series of residues Figure . ExampleUOXFIf linkagePosition==6 then //       position=45;      border=false;      change orientation = false;      sticky=false;CFGIf residueType==Fucose then //       position={-90,90};      border=false;      change orientation = true;      sticky=true;BothIf residueClass==Substituent then //       position={-90,90};      border=true;      change orientation = false;      sticky=false;After the rules have been applied, the positioning algorithm has to decide the actual position of each node among the possible choices. All the children of a residue are considered altogether in order to better redistribute them in the space around the parent. The placement of each node is decided starting from the residues with stricter constraints. Firstly, all the residues with a single possible position are assigned. Secondly, the border residues are sequentially placed in an empty border position if available or in the less crowded position otherwise. When deciding the less crowded position the booking algorithm makes no difference between border and non-border regions. Finally, the remaining children are sequentially placed in the less crowded positions. The automatic placement can be overridden on user request, in case a specific arrangement of the structure is needed to represent particular conditions.The algorithm for computing the bounding boxes of residues is the core component of the rendering process. The bounding box indicates at which coordinates a residue will be displayed on the output media. The procedure is described here only for a right-to-left orientation: the others are simply rotated by multiples of 90 degrees with respect to this . The algorithm recursively navigates through the tree structure performing the following phases for each node Figure :1. compute the bounding box of the current residue with a free spatial placement Figure ;2. recursively compute the bounding boxes for all the sub-trees by grouping for region and align the sub-trees in each region Figure ;3. align centre of border regions -90 and 90 with the current residue and place the regions on the bottom and top of the residue Figure ;4. align left regions  and place them on the left of the current residue without clashing with border regions Figure ;5. align centre of regions -90 and 90 with current residue and place the regions on the bottom and top of the residue not clashing with the other regions Figure .During phase 2 each region is computed separately. Firstly, the bounding boxes are computed recursively for each residue of every sub-tree. Once the bounding boxes of the residues of a sub-tree are computed, they are aligned with the residues of the adjacent sub-tree in the region. The alignment of two sub-trees is computed clockwise . The alignment is performed to minimize the spatial distance between the sub-trees and to avoid collisions between their residues. An example of top-to-bottom alignment of one sub-tree on the right of another is given in Figure 1. align the roots of the two sub-trees on the right of their bounding boxes Figure ;2. compute the size of the shift needed to move one sub-tree on the bottom of the other maintaining a minimum distance between all their nodes Figure ;3. translate all the residues of the sub-tree according to the computed shift value Figure .This alignment and collision solving algorithm is used during all the other phases to align entire regions. The alignment and collision solving algorithm is the most delicate part of the rendering process and it is responsible for the optimal placement of residues.The alignment of left regions  with the current residue has three special cases Figure . If regiThe computation of bounding boxes for the bracket residue and its children (antennae with uncertain linkage) is performed separately. The bounding boxes for these residues are computed after the rest of the structure. The bracket residue is placed on the left of the structure and its bounding box is as large as the whole structure. Its children are aligned top to bottom on the right of the bracket. For each antenna the bounding boxes are computed as for the normal structure.Residues and linkages are drawn as geometrical shapes and not as bitmaps so that resolution is not affected at different sizes and scales. A residue is drawn by fitting the chosen shape inside the bounding box, while a linkage is displayed by drawing a styled line connecting the centre of the parent's bounding box with that of the child. The aspect of residues and linkages is part of the graphical model and it is also stored in configuration files. The residue style matches a single residue type. Various graphical attributes can be set for the residue style: shape , fill colour (RGB format), fill style , associated text and text colour. Some of the shapes, like those for reducing-ends, are oriented to point towards either the parent or the children residue. The linkage style is decided using the same matching operator described for positioning rules. The graphical attributes that can be set for a linkage are: line style , line shape  and which linkage information to display. Anomeric state and linkage position can be displayed: never, always, only if known. Linkage information is displayed near the linkage line, next to the corresponding residue: anomeric state is displayed next to the child and linkage position next to the parent.The automatic rendering algorithm detailed in the previous section enable the "GlycanBuilder" to be employed in several different applications. A visual editor for glycan structures has been developed both as a stand-alone Java application [see additional file Mass spectrometry is the main analytical technique currently used to address the challenges of glycomics  as it ofThe "GlycanBuilder", a software tool for building and displaying glycan structures, has been presented here. The tool is based on an automatic rendering algorithm that can draw glycan structures in a chosen symbolic notation with no user intervention. The symbolic notation is encoded in a graphical model as a set of rules specifying the style and placement of structure residues. The graphical model is independent from the rendering algorithm and can readily be exchanged to specify different symbolic notations. The rendering algorithm is able to produce high-quality compact representations of structures which are ready for publication purposes. The type of structures that can be represented is not restricted to any particular type or biological context. The algorithm is inherently fast and is able to draw a structure using only few traversals of the glycan tree, with no iterative optimization steps. The automated rendering process enables the "GlycanBuilder" to be used both as a user-independent component for displaying glycan structures and as a rapid and easy-to-use drawing tool. The computer encoded structures can be imported into the tool and exported from it using the Glyco-CT format. The "GlycanBuilder" can thus be integrated in other applications to create intuitive user interfaces for input of glycans or to display encoded structures using symbolic notations. The tool is available as a stand-alone Java application [see additional file • Project name: EuroCarbDB – GlycanBuilder• Project home page: • Operating system(s): Platform independent• Programming language: Java• Other requirements: Java 5.0 or higher• License: GNU GPL• Any restrictions to use by non-academics: no licence neededThe author(s) declare that they have no competing interests.AC developed the software tool and tested it, created the website and drafted the manuscript. AD oversaw the project, recognized the validity of the approach, and edited the paper. SH participated in the definition of the requirements of the software, tested the software tool and helped to draft the manuscript. All authors read and approved the final manuscript.The ZIP archive contains the stand-alone version of the "GlycanBuilder" structure editor. To install the tool, extract the content of the ZIP archive in a folder of your choice. To run the tool, double click on the file GlycanBuilder.jar. The tool has been tested under Windows, Linux and Mac OS X. In order to run the GlycanBuilder tool, the Java Runtime Environment (JRE) version 5.0 must be installed on the computer.Click here for file


Microarray experiments, as well as other genomic analyses, often result in large gene sets containing up to several hundred genes. The biological significance of such sets of genes is, usually, not readily apparent.GO), which comprises three disjoint hierarchies for Molecular functions, Biological processes and Cellular locations. The annotations can be used to identify functions that are enriched in the set, but this analysis can be misleading since the underlying distribution of genes among various functions is not uniform. For example, a large number of genes in a set might be kinases just because the genome contains many kinases.Identification of the functions of the genes in the set can help highlight features of interest. The Gene Ontology Consortium  has annoGObar is a web-based visualizer that implements this algorithm.We use the Gene Ontology hierarchy and the annotations to pick significant functions and pathways by comparing the distribution of functions in a given gene list against the distribution of all the genes in the genome, using the hypergeometric distribution to assign probabilities. The public website for GObar  can anal[GObar is freely available as a web-based tool at  and can  Large scale genomic studies, especially expression microarrays, routinely identify large genes sets (sometimes a few hundred or more) of interest. Researchers are faced with the problem of identifying interesting features in such datasets. A listing of gene annotations  can help identify interesting features, but this is impractical with large sets, due to the labor involved and the difficulty in picking statistically significant trends from large datasets. Thus, a user-friendly method is required for the routine analysis of such datasets.GO). There are separate hierarchies for Molecular Functions, Cellular Components and Biological Processes. The terms on the hierarchy share a parent-child relationship in which a child term is either a specific instance or a part of its parent term. The terms get more specific the lower they are on the hierarchy [The Gene Ontology consortium  curates ierarchy . Each nonucleic-acid binding term is shown in Figure GO term browser on the GObar website [nucleic-acid binding, has two children, RNA binding(GO:0003723) and DNA binding(GO:0003677). RNA binding itself has many children, including double-stranded RNA binding(GO:0003725) and single-stranded RNA binding(GO:0003727). DNA binding has double-stranded DNA binding(GO:0003690) and single-stranded DNA binding(GO:0003697) as children.A small portion of the GO molecular function heirarchy around the  website . The GO A single gene can appear in several GO terms. For example, Dicer-1 (FBgn0039016 in D. Melanogaster) has several molecular functions, such as double-stranded RNA binding GO:0003725) and bidentate ribonuclease III activity (GO:0016443) which are unrelated to each other. Figure 5 and biddsRNA) binding genes, then it is intuitively obvious that dsRNA binding is affected by the experiment and pathways using this function might be responding to the experiment. GObar uses the hypergeometric distribution (explained below) to quantify this intuition.In Figure The basic idea of the algorithm is to compare the distribution of the genes from a set on the GO tree against the distribution of all the genes of the genome on the GO tree, identify and highlight branches that are improbably enriched by chance alone, and render an image of the GO tree that will allow user interactions to further explore the data.The GO tree is first populated with all the genes in the genome, which involves placing genes at all the nodes that describe the gene. This operation is carried out only once, and is redone each time the genomic data gets updated. At each node two sets of counts are maintained, the contribution of genes from nodes that are children of the current node (distributed count) and the contribution from the genes at the current node (bare count). The total count at each node (bare count + distributed count), is divided equally amongst the distributed counts of each parent (please note that each node can have multiple parents). To calculate the counts at each node the leaves (nodes with no children) are first considered, and the counts are progressively transmitted up in a level-by-level manner, until the root is reached.N objects of three types, A (count = a), B (count = b), and C (count = c) so that, N = a + b + c, then, a random selection of n objects from these N objects will contain α A objects, β B objects and γ C objects (where α + β + γ = n) with a probability given by the hypergeometric distributionFor the analysis the GO tree is populated with the list of genes to be analysed. Once again a set of distributed and bare counts is calculated at each node for this list. The distribution of these counts is compared to the genomic set and significance is assigned to the deviations from the expected counts, using the hypergeometric distribution, which is now described. If there is a collection of where,This equation can be generalized to arbitrary collections of objects. Using this probability distribution, highly improbable deviations from the expected numbers are highlighted under the assumption that they are likely to have a biological significance.GO data can be downloaded from the Gene Ontology website . The datis_a or part_of to define the relationships.The downloaded GO data is used to populate one table with GO IDs and the ID definitions, and another table with a description of relationships between the GO IDs, which can use terms such as gene2go and gene2accession were retrieved from Entrez Gene [Flybase [In order to associate GO terms with gene IDs (accession), the files rez Gene  for the [Flybase . Each geA table, whose columns are shown in Table • Level (depth in a tree): A recursive depth-first search in a bottom-up fashion is carried out to determine the level of GO terms associated with the experiment, as explained in Figure • Number of trails up: This is obtained from the table of GO ID relationships by counting the number of parents for a node.The following two items are calculated once in the beginning for all the genes in the genome and for each analysis of a gene-list.• BC: Bare count (BC) is a number of genes associated with each GO term (node).BC + DC is propagated to the node's immediate parent. If a node has more than one parent then total count is divided by the number of trails up, which is the same as the number of parents.• DC: Starting from the lowest node(s) in the tree (determined by the Level column), the total count, Entrez Gene for human and mouse data [Flybase [SGD [Entrez Gene, two sets of maps exist, a gene id to GO map and a gene to gene id map. At the end of this process each GO node gets a list of genes. The term bare counts denotes the counts of genes at each node. The genes on children nodes also contribute to the counts on any given node, which are tracked separately and called distributed counts. Thus, the distributed count of a node is the sum of contributions of the nodes below it in the gene ontology hierarchy. Each node contributes the sum of its bare count and its distributed count equally to the distributed counts of each of its parents. This process can be recursively applied, starting from the lowest levels (or greatest depths) of the tree and working the way up the tree.We have populated the GO tree with datasets from use data , Flybase[Flybase  for fly ase [SGD  for yeasIf the accounting of distributed counts is to be done properly, defining the depth of each node in the tree is important. The rule for assigning depth to each node is that, if a node gets multiple levels, then the highest depth is always assigned to it. This can be done by picking the leaves of the tree (nodes with no children) and travelling recursively all the way up to the root (node with no parents). For each path to the top, depth is assigned to each node based on the number of steps to the node from the root. If a node already has a depth assigned to it, then the depth is replaced with the current depth only if it is bigger. This is explained in Figure In order to calculate the distributed counts for each node, the list of nodes is ordered based on their depths. Starting from nodes with the highest depths the counts are propagated up, as described above, summing up the bare count and distributed count and partitioning the sum equally amongst all the parents. After exhausting the list of nodes, all the nodes should have a bare count and a distributed count assigned to them.In order to calculate probabilities for a given experimental dataset, we need to first populate the GO tree with the experimental dataset. A procedure identical to the one used in the previous section is implemented, resulting in a GO tree with just the dataset of interest on it.iBC, i DCbe the bare and distributed counts respectively at node i for the genomic dataset and let ibc, i dcbe the bare and distributed counts respectively at node i for the experimental dataset. Then, for the Node 0 in Figure 8.Let DC0 = (BC1 + DC1) + (BC2 + DC2) + (BC3 + DC3)/2     (3)dc0 = (bc1 + dc1) + (bc2 + dc2) + (bc3 + dc3)/2     (4)The following are defined for ease of notation:N1 = (BC1 + DC1)     (5)N2 = (BC2 + DC2)     (6)N3 = (BC3 + DC3)/2     (7)N0 = DC0 = N1 + N2 + N3     (8)n1 = (bc1 + dc1)     (9)n2 = (bc2 + dc2)     (10)n3 = (bc3 + dc3)/2     (11)n0 = dc0 = n1 + n2 + n3     (12)(13)Then, the probability that a dataset is a random selection from the genes in the genome is given by the Hypergeometric formula (explained above)wheren1 is given byThe expected value for PD as the deviation of the counts on a node i from its expected number and is given byWe define P and PD to prune the trees, as described in the next section.We use Listing all the nodes of the GO tree for a given dataset is not very informative, especially if only a few nodes are populated or if a large number of GO terms are populated by a small number of genes. This also defeats the purpose of helping users narrow down the GO terms of interest.A node can only be pruned if every node under it also satisfies the pruning condition. The tree is pruned using the following rules to make the viewing manageable,n0 <cn, stop traversing the tree, that is, do not show anything below such a node. The population cutoff, c ncan be set by the level of details option on the GObar webpage at step 4, shown in figure Less Detailed corresponds to a minimum of 6 genes, Detailed corresponds to a minimum of 3 genes and Very Detailed shows every node.1. if P > thP. The threshold th Pis arbitrarily set at 0.1.2. Prune nodes that have i ndeviates significantly up from <in>, then the path is hightlighted using red color. PD can be set using the deviation stringency option in step 4 on the webpage, shown in figure Less strict corresponds to a deviation cutoff value of 0.2, Strict corresponds to a cutoff of 0.5 and very strict corresponds to a cutoff value of 0.8.3. if The pruning is done starting with leaves (nodes with no children) on the tree, and stops when it reaches a node that should not be pruned according to the rules above. Fine variations of the pruning conditions are not allowed as these do not offer useful biological information and make the tool difficult to use.SVG) is used to make it interactive. An example of the visualization is shown in Figure Graphviz  is used The tool also allows the downloading of all the genes in the GO tree below any node. The downloaded list is in the form of a comma separated valued (csv) file, which contains the gene, the GO terms for each gene and a short description.In the downloaded list, the uploaded genes are highlighted, since the list will also contain genes that belong to the nodes but are not in the uploaded list.FBgn0034246:Dicer2, FBgn0039016:Dicer1). The website offers several options to limit what is rendered in the result page, but using the default settings is recommended for the initial exploration. After the section of the GO tree relevant to the uploaded dataset is drawn, the website allows the user to limit the view to specific branches of the GO tree . Nodes containing genes from the uploaded list are rendered in a green oval. A node is red if it does not contain any genes from the uploaded list but one of its children node has genes from the list. A pop-up window also shows the GO terms and their levels in the GO heirarchy, that have been highlighted by the analysis. Taking the mouse over a GO term pops-up an information window that can by locked in place by clicking on the left mouse-button. There are links in this information window that can be used to download all the genes on and below the node on the GO tree, with the input genes highlighted in red. If user annotations are given for each gene, then they appear with the gene name in the data downloads. One of the links also allows listing all the GO terms below the node of interest on the tree. The genes in the data download that are not from the uploaded list might also be worthy of further study, especially if many of the other genes in the pathway or GO term are highlighted in the experiment.Gobar is accessible at our website  and can The numbers on the lines between the nodes are the deviation from expected counts . A red line implies that the child node connected to the path in the GO hierarchy is over-represented in the uploaded dataset and the corresponding GO term might be biologically significant.step 4 on the front page. Pruning is the removal of branches of the GO tree which do not carry much information . The result of placing Dicer-1 (FBgn0039016) in D. Melanogaster is shown in Figure If a single gene (or a few genes) needs to be placed on the GO tree, then the tree should not be pruned, which is achieved by selecting the "No" option in We will apply GObar to the analysis of results from a genomic study of splicing . SplicinIf the U12-dependent splice sites persist for some biological reason, then it seems reasonable to assume that only genes with roles in certain functions should contain these splice sites. There has been some speculation, but no rigorous assessment, regarding the functional bias of these genes ,10. A coGTPase regulator activity, cation channel activity and calcium channel activity. The human set also highlighted sodium channel activity and voltage-gated calcium channel activity. In the biological process branch, the analysis highlighted intracellular signalling cascade and actin-filament based process. The Drosophila set was too small (7 genes), to give any detailed statistics, but GObar did highlight transporter activity, which is a parent of the cation channel activity. The highlights that are present in both human and mouse genomes could point to reasons for the persistence of AT-AC U12-dependent splice sites over evolutionary time-scales. We believe this might have some basis in the biological control of the rates of splicing reactions of these genes but reaching a firm conclusion requires an investigation that is beyond the scope of this study.In the molecular function branch, analysis of the mouse set highlighted Our proposed method of analysis is mathematically robust and allows visualization and identification of pathways. We can identify sub-groups of genes that cannot be explained by chance alone. This in turn can identify pathways that are of interest in the process under study. Identification of the pathways then allows study of other genes in the pathway that are not picked up in the experiment, allowing for a clearer understanding of subtle effects and quantifying the errors in the experiment.The conclusions we reach using our method of analysis does depend on the accuracy of the gene annotations. Thus, if the role of a gene in a pathway were unknown, or if a small set of genes could have a strong phenotypic effect , then GObar will mislead the investigator. Such phenomena are, in general, more difficult to study using microarrays and require supplementary biological assays to uncover the underlying mechanisms.Using the GO tree allows us to ameliorate some of the problems inherent in gene annotations, so that genes below the term of interest are also counted as part of the function being studied. Our method involves a one-time analysis of the whole genome dataset, which then allows us to decide, in a straightforward manner, the significance of any number of datasets and allows easy navigation and analysis of the data. The convenience and robustness of the method are the novel contributions here.hits, by allowing genes that are lower down in the tree to be a part of the node under consideration.Another rigorous approach identifies biological themes from gene lists using GO, by calculating the over-representation of categories in the experimental list relative to a background list . A problem with this is the underlying assumption that the GO annotations at each node are accurate. In our approach, in order to allow for the fact that genes may be placed at different depths due to human biases, we let every gene below a particular node contribute to the counts on that node, but done in a way that prevents multiple counting. This also allows genes with more specific functional annotations to contribute to the more general annotation. Thus, our approach also differs from this one in the way we define We offer a detailed comparison with GOstat  availablIn addition, the GO browser offered on the GObar website allows an SVG-based exploration of the GO tree, simultaneously showing all the branches and relationships between them, which is different from the text based version offered by the AMIGO website . GObar aThe analysis of genes with U12-dependent splice sites, given in the previous section, is indicative of the power of this approach. We identified functions that are over-represented in the AT-AC-U12 set, which in turn can be the starting point of an investigation into the phylogeny of the genes involved. The phylogeny could explain the evolution and the role of AT-AC-U12 type splice sites.[GObar is a convenient tool for the analysis of large gene lists. It provides useful guidance to biologists on functions and pathways that need further study and is available freely over the web at .Ravi Sachidanandam conceived the algorithm and the project and helped with the coding. Gurpreet Katari implemented an initial version of the code before leaving his position and Jason Lee improved and revised much of the code and implemented the web-based front end.


Different imaging systems  rely on a wide variety of physical properties, and the datasets obtained from such systems provide only partial information about the unknown true state. One approach is to choose complementary imaging systems, and to combine the information to achieve a better representation.This paper discusses the combination of ultrasound and electrical impedance tomography (EIT) information. Ultrasound reflection signals are good at locating sharp acoustic density changes associated with the boundaries of objects. Some boundaries, however, may be indeterminable due to masking from intermediate boundaries or because they are outside the ultrasound beam. Conversely, the EIT data contains relatively low-quality information, but it includes the whole region enclosed by the electrodes.Results are shown from a narrowband level-set method applied to 2D and 3D EIT incorporating limited angle ultrasound time of flight data.The EIT reconstruction is shown to be faster and more accurate using the additional edge information from both one and four transducer ultrasound systems. Electrical impedance tomography (EIT) seeks to image electrical conductivity distribution of an object by measuring the impedance data between electrodes attached to the outer surface of the body . In thisA standard single transducer ultrasound system would typically only see one face of the ice ball. In contrast, a 3D EIT system would sense the whole volume but the boundaries of the ice ball would be difficult to accurately determine. Full medical ultrasound systems use scanning transducers or phased arrays to produce 2D or 3D images of the body. These systems are expensive and will still only show one face of the ice ball. A simpler and much cheaper system uses a few individual ultrasound transducers placed around the object. This method can estimate the edge position in a few places but cannot produce a full image. This information, however, could be used to improve the reconstruction method of an additional modality, such as EIT. This paper looks to introduce the fusion approach by proposing a narrowband level-set algorithm, which focuses the EIT reconstruction using the ultrasound data.The level-set method was initially introduced for tracking propagating boundaries, but can also be used to locate static boundaries such as the interface between an inclusion and background -11. TherIn this paper the finite element method (FEM) has been used to solve the forward problem of EIT. This is implemented in Matlab using codes from EIDORS2D and EIDORS3D ,17. A trUsing an iterative method, with an update formula for the level-set function, the interface between two materials can be recovered. The level set is known to generate good interface results ,10 but tIn EIT, the differences in the electrical properties, i.e. conductivity distribution inside the object; is used to generate a tomographic image. EIT is used in both medical and industrial applications. The advantage of such a technique over other imaging modalities is such that, it provides a non-invasive  method and requires no ionizing radiation. Furthermore, EIT is a relative low cost and simple functional technique. Moreover, a portable measurement system could also be designed for it. The most important drawback of EIT is its poor image resolution, which is often restricted by the number of electrodes used for data acquisition. Data acquisition is typically made by applying an electrical current to the object using a set of electrodes, and measuring the developed voltage between other electrodes.EIT is suggested to be a good technique for the cryosurgery monitoring. In this article we are reformulating the EIT image reconstruction problem to an interface reconstruction problem between frozen and normal tissue. That would allow us to extract the information we need in more efficient way. Fortunately, this is an application of EIT that the conductivity contrast of the region to be imaged is high and a localised problem as the cryogen probe can tell us where the approximate location of the frozen area is. Here we are attempting to use some additional information about the interface by using ultrasound data.time of flight (TOF) of the ultrasound wave from transducer to the object and back again, and the known velocity of sound in the liquid. Ultrasound technique is based on measuring the TOF of ultrasonic waves arriving at the boundary of the region of interest, which here is the ice ball. Ultrasonic transducers are evenly spaced around the circumference of a body.The ultrasound method is a single pulse-echo measurement . The traWe assume the transducer beam is not diverging  such that only a known narrow strip in front of the transducer is investigated  to describe the front propagation. Instead we have implemented a narrowband level set method . In narrr can be described in terms of the level-set function depending on the position of the point r with respect to the boundary ∂D of the inclusion D asThe conductivity at each point andD = {r:Φ(r) = 0}.     (3)∂int and σext are the conductivity of the inclusion and the background respectively. The describing level-set function is a function form R3 → R for three-dimensional case, and its value is zero on the boundary, it has a negative sign inside and a positive sign outside of the boundary.Here σD (which in turn describes a conductivity distribution) that minimizes the mismatch between the measured and fitted voltage data. The mismatch function, Ψ(∂D), is defined asThe inverse boundary value problem is to find the boundary ∂D) = ||Vm - V(∂D(σ))|| + G(∂D(σ))     (4)Ψ) are the voltages calculated from the conductivity distribution, σ, derived from the corresponding boundary ∂D and G. is a regularisation term applied to the interface. In this paper the regularisation matrix is the identity matrix. The derivative of the voltage to a change in interface was derive [where s derive  and [10,s derive  and has s derive  has been1. Start with an initial guess for the shape of the inclusion, which is an initial level-set function, in our case a circle located in the centre.2. Define the interface and narrow band; the narrow band is an area that includes pixels sharing points with the interface.3. Solve the forward problem and calculate the Jacobian with respect to the boundary.4. Update the level-set function and calculate a new interface boundary and narrow band.5. Check the misfit in the data and if the error is small enough then stop.6. If the misfit is not small go to Step 2In general for EIT to be used in cryosurgery monitoring a higher resolution EIT image is needed. Figure Figure Figure For enhanced EIT reconstruction using single ultrasound transducer measurements we assume that the bottom edge position of the ice ball is known. For the multiple transducer-enhanced reconstructions we include four edge positions as shown on the right in Figure The number of iterations in the level-set method is a function of the initial guess. If the initial guess is far from the real object it will take more iteration to recover the shape. The real object  m, on the boundary of the inclusion is identified and used to help start the level-set algorithm. The initial guess is of a spherical object with diameter 0.07 m centred at  m. The convergence improves .2. Evaluate an approximated speed profile for the ultrasound based on conductivity profile (approximate it to temperature gradient) generated using linear EIT image and consider the speed with different organs based on EIT image.3. Calculate some points at the boundary of the inclusion (here is frozen tissue) using ultrasound data.4. Applying narrowband level set method as a more accurate interface reconstruction technique using EIT data and a priori ultrasound data.With above strategy, we have an EIT image but with less accurate interface information and a localised conductivity interface reconstruction with more detailed information about the interface between frozen and normal tissues. Combination of full ultrasound tomography with consideration of different speed of ultrasound in different organs and different temperature is our main aim for future study.