Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained. Human observers are able to process a considerable number of visual stimuli. However, they focus their attention on certain areas of the scene or some objects that are either markedly distinct from their surroundings  or relevant for the task they are performing . In the first example, the subject’s visual attention mechanisms are driven by the inherent features of the stimuli (differences in shape and color from the surroundings). This is a typical instance of bottom–up visual attention . In the An entire branch of computational vision has originated from the interest in characterizing and mainly also predicting the areas of interest for a given subject when looking at a scene . Moreover, numerous models have been developed and tested for their performance in two main tasks: saliency detection  aIn general, saliency prediction is considered to be a harder task than saliency detection. Both kinds of visual attention models  have had similar trends in their evolution. The initial period was marked by the extended use of visual features of different types  previouThe accuracy of the models is determined by using a database of annotated images  or a database that comprises as well the gaze fixation maps or heat maps for the given image set, obtained usually with the aid of a fixation tracking device or eyetracker  for the For color (RGB) images, there are numerous kinds of saliency image databases, including the MIT Saliency Benchmark , SALICONRecently, saliency models have been expanded to accept multispectral images as input   and their corresponding saliency maps. The database is composed of two sets of images. The first set contains 1147 multispectral images together with their RGB versions, as well as the raw heat maps of 10 observers recorded with an eyetracker while performing free viewing. The second set contains an additional set of 136 higher-resolution multispectral images together with their RGB versions, as well as two sets of heat maps: one generated during free viewing and the other during category counting. For both sets, the raw heat maps and the filtered heat maps are provided observer-wise and accumulated  saliency maps and introduce a correlation study between saliency map comparison metrics and image complexity metrics. Besides, an example comparison is also performed with an existing saliency prediction model  using boThe paper is structured as follows: in The multispectral images were captured using a filter-wheel-based multispectral camera model, PixelTeq Spectrocam VISNIR  . This caIn each capturing sequence, three images were captured and averaged for each filter in order to reduce the impact of noise. Exposure times ranged from 1 ms to 2 s, depending on the scene and the filter.Each eight-band multispectral image had a bit depth of 12 bits. It was stored in a floating point format with values ranging from 0 to 1. Besides, three channels were selected to become red, green, and blue  channels of the color images. They were the ones corresponding to filter numbers 6 (red), 4 (green), and 2 (blue). Each color channel was individually normalized to range  as a manual white balance. Alterations in the apparent color of the objects introduced by the selection of these three particular channels as RGB bands did not compromise the tasks performed in the experiments presented in this study. The final appearance of the RGB images built was pretty natural, and no color artifacts were introduced due to the selection of these spectral bands as red, green, and blue channels, as can be seen in the example RGB images presented in The color image rendered was stored in an 8-bit jpeg format with no compression.A total of 104 scenes were captured using the method explained in The eyetracker device used allowed the recording of a huge amount of information during the gaze experiments. Separately for each eye, the device sampled its position 60 times per second. In each of these samples, the device recorded not only the horizontal and vertical coordinates of the gaze point but also the type of movement the eye was doing (fixation or saccades), the time stamp, the pupil size, the distance to the screen, and the validity of the record. All this information ended up in a big amount of data to be processed before retrieving the final fixation map. A heat map is a three-dimensional array. The first two dimensions correspond to the horizontal and vertical coordinates of the image displayed in the monitor . The third dimension is related to the fixation time in those spatial coordinates. This fixation time is at the same time related to saliency. The more salient the content of a given area of the image is, the longer the fixation time within the spatial coordinates of this region of interest is.Out of the available information, the heat maps were built using the data from the fixations only. The duration of each eye movement was calculated as the difference between the time stamps of two consecutive movements. Then every movement that was not a fixation was filtered out. After that, also those fixations whose binocular validities were not positive were filtered out. Validity is a binary variable whose value is 1 (positive) if both eyes are sufficiently opened and the eyetracker is detecting them correctly. Otherwise, its value is 0 (negative).With the remaining valid fixations, the monocular spatial coordinates X and Y were retrieved. Since these coordinates were not the same for both eyes, the mean position was calculated, and this was used as the real fixation position. With these spatial locations and their corresponding fixation times, we built a raw heat map placing deltas (step 3D functions of 1 × 1 pixel width) whose heights equaled the fixation time in those positions. Once these preliminary heat maps were built, we convolved them with a two-dimensional Gaussian filter of radius R = 100 pixels and standard deviation σ = 20 pixels. Thus, the full width at half maximum (FWHM) equaled 48 pixels. Since the spatial resolution of the monitor was 4.26 pixels/mm, the angle subtended by the Gaussian curve at half maximum was 1.075o at the viewing distance used in the gaze recording experiments. This was in accordance with previous works by other authors . In FiguFor each observer, the raw delta heat maps and the filtered heat maps were provided, and for each image, the accumulated heat map was calculated, summing up all deltas from all observers. The raw and filtered versions of these accumulated heat maps were provided as well. Both the raw delta heat maps and the final filtered heat maps were provided in case the users would like to create a differently filtered heat map. As explained in Using the 44 scenes captured in the first campaign, a double experiment was performed to assess whether the saliency information provided by the heat maps was different depending on the task that was asked of the observers. Each image was divided into 4 sub-images (1228 × 1029 pixels each), and then those whose content was not significant  were deleted, resulting in a set of 136 sub-images. The display size on screen was 290 × 240 mm. Each image was displayed for 6 s, and between images, a central white cross was displayed for 2 s. The observers were asked to fix their gaze at the center of this cross in order to have an initial central gaze point for every image.In the first round of this experiment, the observers were asked to just freely look at the images with no purpose other than to see what was the content in them (free-viewing task). In the second round, the observers were asked to count the number of categories present in the scenes (categorization task). There were seven categories: buildings , people, vehicles, vegetation, traffic signs , urban furniture , and other (whatever was not included in the previous categories). The observers were asked to disregard both the asphalt or ground and the sky when counting the categories. In Using the 104 scenes captured in the two campaigns, a second simple experiment was performed. Now the images were divided into 16 sub-images (614 × 514 pixels each), and those whose content was not significant were deleted, resulting in a set of 1147 sub-images. The display size on screen was 145 × 120 mm. Each image was displayed for 4 s, and between images, a central white cross was displayed for 1.5 s for the same purpose as in the first experiment.The whole experiment took 38 min and 8 s per observer . Ten observers performed the experiment, and for each image, the 10 observer-wise heat maps and the accumulated heat maps were stored.Now the observers were only asked to freely look at the images to see the content in them. For each observer, a total image display time of 1 h, 45 min, and 9 s was taken. The observers could complete the whole experiment in different sessions of variable lengths since they could control the ending of each session. Each image was displayed only once for each observer.Using the 136 RGB and multispectral images created for the experiment described in Area under the curve (AUC): three different versions of this metric were computed: AUC-Borji (AUCB) , AUC-JudNormalized scanpath saliency (NSS) : averageInformation gain (IG) : compareA correlation study was carried out between heat map comparison metrics and image complexity metrics to test whether the hypothesis that more complex images increase differences in the way observers look at the images is true. Additionally, inter-experiment and inter-observer (intra-experiment) comparisons were made among heat maps. These metrics are designed to compare a heat map with a ground-truth saliency map. In our case, heat maps were compared two by two. All the metrics were computed using one of the compared heat maps as ground truth and the other as test heat map. The heat map comparison metrics studied  are the Although there are many different kinds of metrics developed or used for heat map comparison , we chosI represents the image, L represents the three pyramid levels used, h(S) is the HOG value for a sub-image, and r(SP) corresponds to the parent of sub-image S [Self-similarity: self-similarity compares the histogram of gradients (HOG) across different equally sized sub-images of the original image . The HOG-image S .maxG is generated as shown in Equation (2).x, y) for each R, G, and B image component. Finally, the complexity of an image is computed as the mean norm of the gradient across all orientations over Gmax [Complexity: by computing first the maximum gradient magnitudes in the image channels, the gradient image ax , as showBirkhoff-like metric: this metric computes the amount of effort the human visual system has to put into the processing of an image. Following on from , the BirAnisotropy: calculated as the variance of all the HOG values at level 3 as explained in . This meEntropy: this is a statistical measure of the randomness present in an image. The higher is its value, the more complex the image is supposed to be ,40.Complexity metrics have been used in early studies to analyze the representation of scene properties directly related to the subjective impression—aesthetic—of images . In ordeFirst, we analyzed the similarity between the fixation mapsin the case of inter-observer and inter-experiment comparisons. Next, the results of the correlation of the heat map comparison metrics with the image complexity metrics would be shown.The five heat map comparison metrics explained in Inter-observer comparison: heat maps were compared between different observers performing the same task two by two. Since there were 10 observers, there existed 45 different two-by-two comparisons. All of them were made, and the average metric values are shown in Inter-experiment comparison: here, the heat maps were compared in two different ways. First of all, for each image, all gaze data from all observers were accumulated in a single raw delta heat map, and then filtered accumulated heat maps from the categorization and free-viewing experiments were compared  for each heat map comparison metric across all image complexity metrics.2 correlation coefficients are very low for all experiments and all metrics studied. These results refute the hypothesis that more complex images result in higher differences in the way observers look at the images.As shown in As explained in There are some clear differences in the information contained in the images corresponding to each spectral band with respect to the RGB image  and NSS  but not significant for the IG metric .The results of the comparison between RGB and single-band heat maps are shown in This article presents a database of eight-channel visible and near infrared (VIS+NIR) multispectral images of urban outdoor scenes together with saliency maps measured using an eyetracker device on different observers. The database can be downloaded and is made publicly available from . Two setBesides, three example experiments were presented as instances of what this database can also be used for. Results from these experiments show that the differences found in the saliency maps were not significantly higher or lower if we compare saliency maps between different observers or between different tasks performed by the same observer. On the other hand, we also found that the complexity of the images was not related to inter-observer variability, meaning that more complex images do not yield higher differences in the way observers look at them. This lack of correlation between image complexity metrics and saliency map comparison metrics happened in both inter-observer (different observers performing the same task) and intra-observer (same observer performing different tasks) comparisons. We also performed a comparative analysis of the heat maps obtained with the RARE model for the RGB image and the different spectral bands. Our previous study  showed t


Numerous surgical techniques are approved for the surgical treatment of benign prostatic obstruction (BPO). They include minimally invasive procedures such as the newly introduced prostatic urethral lift and water vapor thermal therapy, transurethral resection, vaporization or enucleation of the prostate and open or laparoscopic/robotic assisted prostatectomy and have been recommended by the guidelines of the most distinguished scientific organizations , 2. In cGuidelines recommend that either monopolar or bipolar TURP may be used, for patients with a moderately enlarged prostate, of up to 80 cc, depending on the surgical expertise of the practitioner , 2. In bMany studies have been published in recent years exploring the use of B-TURP and comparing it with M-TURP. Systematic reviews have also compared the two techniques, confirming comparable efficacy for both and a reduced risk for acute dilutional hyponatremia and TUR syndrome for B-TURP , 6. AlthThere are different bipolar resection devices and no evidence in favor of a specific system . In the An important aspect of the present study was the strict methodological criteria adopted. Based on the Jadad scale that assess the methodological quality of randomized control trials, most previous studies comparing PK-TURP with M-TURP had one or more methodological issues . The preAnother potential problem that deserves attention is the fact that the study was conducted in a university hospital and surgeries were performed by practitioners with varying levels of experience. It certainly might be seen as a limitation, but the fact that it provides the outcomes of both surgical techniques in the daily practice is relevant and the fact that a sub-analysis based on the level of surgical experience did not show differences in primary and secondary outcomes is reassuring.Finally, since cost-effectiveness studies are very important to determine the value of technologies and treatments, and guide public policies for patient management, it is a little frustrating that the authors did not look at this aspect in the study. A recent systematic review comparing M-TURP with B-TURP using a different energy source favoured the B-TURP.  There ar


Multiple carpometacarpal dislocations with a simultaneous fracture of the hamate represent less than 1% of all injuries to the hand and wrist regions, with a scarcity of published cases. These injuries usually require a great force, and diagnosis can be missed or delayed because of the high likelihood of other severe concomitant injuries. We report a case of acute closed dislocation of the third through fifth carpometacarpal joints and an associated fracture of the hamate in a military paratrooper. The injury was caused by a wrong landing technique during parachuting. The patient was managed with primary surgical repair, and after a six-month follow-up, he has excellent functional results. The fact that both this clinical entity and the mechanism of injury are very unusual a high index of suspicion is needed, especially for orthopedic surgeons working in military hospitals. Additionally, given that there is a paucity of published cases and optional treatment is controversial, this study corroborates the superiority of surgical repair in a long-term basis. Carpometacarpal (CMC) dislocations of the hand with a simultaneous fracture of the hamate have been infrequently cited in the medical bibliography occurring in less than 1% of osseous hand injuries. The most common mechanism of the injury is the hyperflexion of the metacarpal heads. These injuries usually require a great force, and diagnosis can be missed or delayed because of the high likelihood of other severe concomitant injuries. Applying a high index of suspicion and performing a good clinical examination play a prominent role in recognizing this rare condition. Treatment is controversial and can be either conservative or operative . We repoA 22-year-old right-handed paratrooper was presented to the orthopedic emergency department because he fell on his outstretched right hand after having attempted a wrong landing technique. The accident took place in the island of Rodos where he received first aids and was then transported to our hospital for further treatment.On physical examination, there was swelling and pain over the dorsum of the hand. Despite the swelling, it was feasible to notice deformity and to palpate the characteristic step-off created by the bases of the metacarpals. Active movement of the wrist and the fingers was limited while the patient was unwilling to cooperate due to pain. Neurovascular assessment revealed both a strong palpable radial pulse and a lively capillary refill while sensation of the hand was normal.Plain radiographs  of the right hand showed complete dorsal dislocation of the three ulnar CMC joints . In ordeAt the emergency department, closed reduction was attempted by applying longitudinal traction to the involved digits with pressure over the bases of the dislocated metacarpals. After that, the wrist was immobilized with a plaster. Three days later, the patient was operated under general anaesthesia in supine position. The surgical treatment chosen was open reduction and internal fixation (ORIF). Two longitudinal incisions were made over the second and fourth web spaces addressing adjacent respective joints. At this point, it is of note that when the subcutaneous hematoma was evacuated and the extensor tendons were retraced with the surrounding loose connective tissues, we noticed that our initial closed reduction which took place in the emergency room has been lost. Therefore, a new one was achieved under image intensifier guidance, and then, internal fixation was accomplished with K-wires 1.6. In detail, the third and fourth metacarpals were transfixed on the trapezoid and on the capitate, respectively. The fifth metacarpal was pinned similarly on the hamate . RegardiBoth the splint and the K-wires were removed in six weeks' time Figures . At thisMultiple carpometacarpal dislocations with a simultaneous fracture of the hamate are uncommon incidents, with a scarcity of published cases. They represent less than 1% of all injuries to the hand and wrist regions –6. AdditDislocations of the CMC joint can be easily missed in the acute setting. Thus, it is recommended that three radiographs should be taken: a dorsopalmar, a true lateral, and an oblique view of the hand. However, a computed tomography (CT scan) is sometimes necessary in order to confirm the diagnosis and show missed carpal bone fractures, assisting in this way to preoperative planning , 11.Because of the paucity of the published cases, treatment of carpometacarpal injuries is debatable. Two case reports by Horneff et al. and Agarwal A. and Agarwal R. demonstrated satisfactory results with closed reduction and splinting regarding the maintenance of reduction and the recovery of hand function , 13. OpeCarpometacarpal (CMC) dislocations of the hand with a simultaneous fracture of the hamate comprise a rare injury. Despite the fact that the landing phase of a parachute jump mostly causes injuries in the lower extremities , upper e


Radiation shielding films used in medical institutions are manufactured by mixing polymer materials with eco-friendly shielding materials. However, it is not easy to distribute the shielding material particles uniformly during the process. The uniform distribution of the shielding material particles is key to the reproducibility of the shielding performance of the films. Therefore, in this study, an air pressure mirroring particle dispersion method was developed to maintain a uniform distribution of the shielding material by dispersing the shielding material on a curved reflector through an air nozzle. The particle distribution state, density, and shielding performance of the cross-section and surface of the shielding films developed using the single-sided dispersion, double-dispersion, and air pressure mirroring particle dispersion methods were evaluated. Compared to the conventional single-sided distribution method, the shielding film produced by the air pressure mirroring particle dispersion method increased the particle packing by 41.5%, density by 12.9%, shielding material content by 22.2%, and shielding performance by 21.4%. Thus, the proposed dispersion method enables better shielding performance through uniform dispersion of shielding material, which is the most important parameter in the manufacture of low-dose shielding films. In particular, radiation workers, including radiologists, nurses, and isotope handlers, are classified as medical personnel with a high risk of exposure to radiation and are regularly monitored through radiation instrumentation3.With the increasing use of radiation in medical institutions for diagnosis and treatment purposes, the potential radiation risks for associated medical staff are also increasing5. Traditionally, radiation workers have actively defended themselves against direct radiation exposure, for example, by maintaining a certain distance or wearing radiation shielding clothing. However, scattering rays, which typically occur in spaces where medical radiation is used, are low-dose indirect radiation, which also increases the risk of exposure. It has been reported that the sources of the scattering rays are the aperture and leakage from the X-ray unit as well as the patient’s body and the examination table. The dose from spatial scattering rays is 1.37–0.05 mGy/min, corresponding to a low radiation dose6. Low-dose radiation, such as natural radiation, is typically less than 100 mSv7.The energy of X-rays used in the diagnostics is in the range where the photoelectric effect and Compton scattering occur frequently. The photoelectric effect occurs more frequently as its energy of radiation is lower and the atomic number of the photoelectron is higher, and is absorbed after interaction with a substance. In addition to the energy of the incident X-rays, Compton scattering also affects the radiation dose received by radiation workers by changing the traveling direction of radiation and thereby increasing the radiation dose to areas that are not directly exposed to the radiation sourceThe existing radiation shields are mainly made of lead. However, they are heavy and uncomfortable for daily wear. Therefore, a considerable amount of research is being conducted on lightweight materials. In fields where an X-ray generator is used at a distance, a shield is required for protection against the scattered rays rather than direct rays. It is necessary to investigate methods for shielding body parts that are not the direct examination areas in addition to designing shielding suits to shield low radiation doses for medical personnel who assist at a certain distance, such as an angiography room.9. Tungsten, which is used as a lead substitute material in the manufacture of lightweight radiation shielding sheets, has a density of 19.3 g/cm3 and exhibits excellent radiation shielding performance, heat resistance, and abrasion resistance10.The transmission energy of scattered rays is weaker than that of direct rays; therefore, lighter radiation shields can be used. Heat-treated molded sheets that were conventionally used for radiation shielding purposes, were made of powdered lead and rubber; however, the lead can be replaced by bismuth, tungsten, antimony, barium, etc. Recently, products using tungsten have proven to be effective12. Moreover, owing to the low thickness of the film, it has excellent workability and flexibility, and is expected to expand the range of use of various radiation shielding applications.In this study, instead of using a mixture of basic materials and polymeric compounds, tungsten nanopowder was distributed directly on film to produce a shielding film. Although it is insufficient for direct radiation, it is expected to be effective in shielding from low-dose indirect radiation. For the adsorption of tungsten nanopowder onto the film at high densities, it is important to consider the size and dispersion method of the powder particles. When the shielding material (tungsten) is high-filled, the density of the radiation shield increases and its thickness decreases; however, it can still easily block low-dose radiation14. To compensate for these shortcomings, this study has developed an air pressure mirroring particle dispersion method that directly disperses tungsten nanopowder on a film. The proposed air pressure mirroring particle dispersion method distributes the tungsten nanoparticles and air at high pressure on the film placed on curved reflectors, through two nozzles, thereby evenly distributing the constantly-sized tungsten nanoparticles and increasing the density of the shielding material. In order to understand the difference in the process between the shielding film made by dispersing the tungsten nanoparticles in the cross-section of the film surface and that made by dispersing the tungsten nanoparticles in two forms on both sides, each shielding film was produced and the distribution of the tungsten nanoparticles was observed. In addition, high-density radiation shielding can be developed by evaluating shielding performance and presenting a powder-filled molding method for producing a shielding film with low dose film thickness. We would like to present the relationship between the shielding performance of the radiation shielding film developed through such a process and the air pressure mirroring particle dispersion method, and propose standardization of the processing technology.The existing shielding material mixing process has the disadvantage of increasing the porosity between the powder particles and decreasing particle packing (PP); moreover, the shielding performance is not uniform owing to the uneven application of particlesThe shielding films made using the tungsten nanopowder exhibit the characteristics presented in Table As illustrated in Fig. The distribution states of the tungsten nanoparticles in all three types of shielding films are depicted in Fig. Next, the medical radiation shielding performances of the three types of films were evaluated. The X-ray effective energy used is presented in Table 15. There are three factors for radiation defense: time, distance, and shielding, but active shielding is the best way for protecting healthcare professionals working with patients16. From the patient's perspective, radiation exposure cannot be completely avoided for diagnostic and therapeutic purposes; however, one desires to have active protection with minimal exposure.Recently, in the medical field, with the development of preventive medicine, the number of tests using medical radiation has increased significantly; hence, exposure to medical radiation is also increasing17. Moreover, the dose distribution varies depending on many variables, such as the energy level of radiation, the structure of the imaging room, the area of the examination room, and the imaging site18. Therefore, exposure to medical radiation, which corresponds to low doses, can easily occur and can be dealt with thin shields.X-rays are absorbed by substances via the photoelectric effect, Compton scattering, electron pair generation, etc., during interaction with the human body, and the ionization makes shielding difficult in comparison with charged-particle radiation19. The most common type of shield is a sheet made of synthetic resin mixed with metal powder. Another method involves making a sheet with different shielding materials to form a composite and then reconstructing the sheet into multiple layers.Thin shields are mostly made of bismuth, tungsten, antimony, barium sulfate, etc., using synthetic rubber and resin, among others, and a method for reducing the radiation pinholes by uniformly dispersing the shielding materials or increasing the density of shielding has been proposedThe most important process parameter during the production of radiation shields is the density of the shield material, which has a direct relationship with the shielding performance. In this study, we proposed a processing technology for dispersing tungsten nanopowder on a PE film to satisfy the reproducibility of the shielding performance, and thereby tried to maintain the same density. The air pressure mirroring particle dispersion method was proposed to disperse the tungsten particles as evenly as possible on the PE film during dispensing. As depicted in Fig. 20. The development of the processing technology of the radiation shield is related to the reproducibility of the uniform shielding performance in addition to the improvement of the shielding performance. Reproducibility and uniformity of the shielding performance are at the core of the radiation shielding film manufacturing technology. Even with the same shielding material and base material, there will be a difference in shielding performance depending on the manufacturing process. The shielding performance is affected by the composition of the shielding material and the polymer material and the degree of the additive reaction. This is because an even arrangement of particles reduces air gaps and can maintain the reproducibility of shielding performance. The dispersion method developed in this study is not a method of mixing a shielding material and a polymer, but a method of depositing and fixing the shielding material on a film. Therefore, the thickness of the shielding film can be adjusted, and the shielding performance can be predicted based on the content of the shielding material. The air pressure mirroring particle dispersion method makes it possible to realize a standard processing technology in which the shielding performance can be determined by the total amount of the shielding substance and the thickness of the shielding film. The shielding performance of currently used films is expressed by its lead equivalent, and it is difficult to predict the shielding performance from thickness. The method proposed in this study is expected to be able to solve this limitation.Dispersion of the shielding material is a key issue in the process of manufacturing the shielding film by mixing the shielding material and synthetic resin. For this purpose, various process techniques are used, such as mixing ratios and steps with additivesThe air pressure mirroring particle dispersion method showed satisfactory results for the improvement of shielding performance and reproducibility of uniform shielding performance.The air pressure mirroring particle dispersion method was proposed in this study for maintaining the reproducibility of shielding performance of eco-friendly radiation shielding films used in medical institutions improved the particle packing and uniformity of shielding materials. In this study, the shielding film, produced by applying the air pressure mirroring particle dispersion method, was compared with the shielding films produced by applying the double-dispersion and single-sided dispersion methods. The shielding material PP, particle distribution, density, and shielding performance of the shielding film produced by applying the air pressure mirroring particle dispersion method was superior to those of the shielding film produced by applying the single-sided dispersion method.21:The radiation intensity is affected by the distance and direction from the source generation point. The transmitted radiation intensity can be calculated using the thickness and density of the shielding film as followsRadiation Intensity is attenuated through interaction with the shielding material as it passes through the material. Thus, by increasing the thickness or density of the medium, the radiation intensity can be lowered, and the shielding performance could be enhanced.22:The absorption coefficient of the shielding film is directly related to the density and thickness of the shielding film containing the shielding material. This relation is derived as followsAs shown in Eq. , to impr23. The equation for obtaining the PP can be expressed as follows:While manufacturing radiation shields, the density of the shielding film can be varied by controlling the thickness and area of the shielding film and by controlling the amount of dispersion on the shielding material. Thus, one way to decrease the thickness while maintaining shielding performance is to increase the PP of the shielding material at a constant volume24.The sizes of the tungsten nanoparticles manufactured externally (2020 BCTech co.Ltd) for this experiment are in the range of 120–480 nm. The reason why the same size is not used for all particles is because the particles are arranged freely according to their size to reduce air gap inside the shielding film. A PE film was used as the base material for the shielding film. When a radiation shielding sheet or a shielding film is produced using tungsten nanopowder, residual pores are generated. Radiation shields with residual pores show low fatigue resistance, poor radiation shielding performance, and limited flexibility. A PE film of thickness in the range 0.015–0.010 mm was used to compensate for these shortcomingsThe air pressure dispersion method for uniformly dispersing tungsten nanoparticles in the PE film is illustrated in Fig. In this study, the tungsten shielding films used in the experiment for comparison were manufactured using three air pressure dispersion methods, and the influence of the dispersion state of the particles on the shielding performance was analyzed. The first shielding film was produced via the single-sided dispersion method, which also used an air nozzle for dispersion of the shielding material; however, the particles were dispersed by placing the PE film on a flat reflector. The second shielding film was produced via a double-dispersion method, in which tungsten nanopowder was divided in half and dispersed on a flat PE film twice. Finally, in the air pressure mirroring particle dispersion method, tungsten was dispersed through an air nozzle and the PE film was placed on a curved reflector.2) according to the particle distribution inside the shield, as shown below25:3), 3), The density of the tungsten nanopowder can be expressed as the relative density (RD) per unit area  was used to identify the particle distribution and the internal structure of the manufactured shielding films. Using FESEM, SEM image was analyzed under 15.0 kV × 10.0 k conditions.It was also estimated that reducing the dispersion speed of the nozzle and narrowing the gap between the nozzles would reduce the air gapAlthough the intensity of radiation used in the medical field is minimal, it has the ability to penetrate the human body. In this shield performance evaluation, the tube voltage, which accounts for the transmitted energy, was used as a reference, and the evaluation was performed using energy in five stages from 40 to 120 kVp. The experiment was conducted based on a tube current of 160 mA and an irradiation time of 60 ms. In addition, all X-ray energies used to obtain accurate transmitted energy were converted into effective energy and applied.27.The geometric arrangement for measuring the effective energy of X-rays is shown in Fig. mentclass2pt{minim28. In this equation, W is the dose measured when there is a shielding film between the X-ray tube and the Dosemeter, and W0 is the irradiation dose value measured when there is no shielding film between the X-ray tube and the Dosemeter. A total of 10 experiments were performed using an X-ray generator  to obtain the transmissive dose values and the average of these values, given in Table The experimental setup to inspect the shielding performance of the three shielding films is shown in Fig.


Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate’s cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant. In human infants, the emergence of sleep cycles occurs at approximately 26 to 28 weeks postmenstrual age (PMA) . During Most prematurely born infants stay in the neonatal intensive care unit (NICU) during the first critical weeks of rapid growth and development of the brain. In the NICU, neonates are exposed to a myriad of noxious environmental stimuli, such as high noise and light levels and painful procedures, which might disrupt their sleep state organization. In recent years, there has been an increasing interest in strategies to promote sleep in the NICU environment  ,9.Real-time automated identification of behavioural states can be used to optimize the planning of NICU caregiving in order to reduce disturbance of sleep-wake cyclicity . MoreoveA number of algorithms for sleep stage classification in preterm neonates have been developed. The majority of these approaches are supervised and combine a set of electroencephalography (EEG) features  with a classification algorithm ,12. ReceThis paper proposes a novel unsupervised method to discriminate quiet sleep from non-quiet sleep in preterm infants. In this study, a tensor-based method exploiting the differences in EEG complexity between different vigilance states will be used. Due to the increasing amount of data being collected, and the specific properties of tensor decompositions, multiway analysis has received increasing attention during recent decades. Tensor algebra has been used in a broad range of applications, such as image and video processing, machine learning and biomedical applications ,16. To tThe remaining part of the paper proceeds as follows. The first section of this paper will describe the dataset. It will then go on to the explanation of the different steps of the proposed method. Afterwards the results of the algorithm will be reported and discussed.The proposed method is evaluated on a dataset consisting of EEG signals recorded at the neonatal intensive care unit of the University Hospitals of Leuven, Belgium. All neonates included in the study were born between 2012 and 2014 at a gestational age below 32 weeks (range: 24 weeks 4 days–32 weeks). In total 97 multichannel EEG signals were measured from 26 neonates at a postmenstrual age (PMA) between 27 and 42 weeks. So, each subject had at least two serial EEG recordings during their stay in the NICU. The study was approved by the Ethics committee of the University Hospitals of Leuven and parental consent was obtained for all recruited patients. Criteria for selecting the subjects were as follows: a normal neurodevelopmental outcome at 9 and 24 months corrected age , no severe brain lesions assessed by ultrasound and not taking any sedative or antiepileptic drugs during the EEG registration. EEG was recorded using nine electrodes: Fp1, Fp2, C3, C4, T3, T4, O1, O2 and Cz using the modified 10–20 system . ElectroQuiet sleep periods were annotated by two independent expert clinicians upon agreement. All other sleep states are merged and will be referred to as non-quiet sleep. The goal of the proposed algorithm is to automatically label EEG segments as either quiet sleep (QS) or non-quiet sleep (NQS). The most important feature in discriminating quiet sleep from non-quiet sleep is the continuity of the EEG trace. Generally, the EEG signal is relatively more discontinuous during quiet sleep compared to non-quiet sleep. Moreover, non-quiet sleep is characterized by a higher variability of the cardiorespiratory pattern and more body movements .The pipeline of the proposed algorithm consists of the following six steps: (1) Preprocessing of the EEG; (2) Assessment of the EEG complexity via computation of the multiscale entropy; (3) Tensorization of each EEG recording, (4) Decomposition of the multiscale entropy tensor; (5) Selecting the component of interest; and (6) Postprocessing and clustering of the temporal signature. Each step of the algorithm will be extensively described in the next paragraphs. Finally, the metrics to assess the classification performance will be explained.In order to avoid distortion of the EEG time series by high or low frequency artifacts, a bandpass finite impulse response filter between 1 and 40 Hz was applied on each EEG channel. Moreover, an additional notch filter at 50 Hz is used to remove any remaining powerline interference. The filters were applied in both forward and reverse directions in order to avoid phase distortion. The EEG signal is then downsampled by a factor of two to reduce the computational complexity. No advanced artifact removal or visual preselection of data has been performed.After filtering the data, the EEG signal is segmented into nonoverlapping windows of 100 s . To asseThis actually corresponds to a moving average operation with a window of length m consecutive data points matching within a tolerance r, will also be similar when an additional data point is added to the sequence. For a discrete time series of length N, this can be expressed as [A and B denote the number of matches of length m + 1 and m within tolerance r, respectively. In this study, the template length m is set equal to 2. The multiscale entropy (MSE) is computed for scales ranging from 1 to 20. The tolerance r is defined as 0.2× the standard deviation of the original time series [Following the coarse-graining of the EEG segment, the regularity of the signal at each scale essed as :(2)SampEe series ,22. OnceThe entropy values of each EEG recording are then organized in a third order tensor with modes: channels, scales and time segments. So, the multiscale entropy curves of consecutive time segments are stacked in the third mode of the tensor Throughout this paper, a scalar, vector, matrix and tensor will be denoted by lowercase letters (a), boldface lowercase letters  or parallel factor analysis (PARAFAC) of a rank- tensors . This caThe advantage of CPD compared to matrix factorizations is that the decomposition is unique  under mild conditions . MoreoveThe CPD of the third-order multiscale entropy tensor with rank R will result in three non-negative factor matrices: the spatial signatures will form the columns of Starting from the assumption that the EEG complexity is different depending on the sleep stage , we expeDue to the fact that  is a nonitten as ,27,28:(5it represents the number of iterations (in this case 50). So, ith and jth iteration are. Finally, the solution of the iteration with the highest similarity to the other repetitions is selected as the stable and reproducible solution.The R components of each repetition are sorted based on  to accouOne of the principal challenges when applying tensor decompositions is selecting an appropriate number of components for the problem at hand . A multiWhen decomposing the multiscale entropy tensor, we expect that one component will reflect the sleep staging of the neonate. However, if the number of components R is greater than one, an automatic selection of the component of interest is required. So, the goal is to find the temporal signature of the component of interest. In We expect that the component related to sleep staging will have a more regular, cyclic pattern compared to the other (noise) components. Therefore, the autocorrelation functions of the temporal signatures are computed. Since the temporal signature reflecting the sleep staging has a stronger correlation in time, we expect that its area under the absolute value of the autocorrelation function (ACF) will be larger ,32. TherOnce the non-negative polyadic decomposition of the multiscale entropy tensor is performed, the temporal signature of the selected component is used to define the neonate’s sleep stage. This temporal signature shows a (slow) cyclic pattern reflecting the sleep staging of the infant. However, there are high frequency oscillations superimposed on this pattern, which could lead to incorrect sleep stage identification. Therefore, a postprocessing step consisting of smoothing the temporal signature using a moving average filter with length L equal to 5 is applied. This moving average filter is applied in both directions (to avoid phase distortion), so actually a weighted moving average filter is used with triangular shape and length 2 × L. This smoothing operation accounts for the fact that a sleep stage does not change instantly. More specifically, sleep periods were only visually labeled as quiet sleep if 3 consecutive minutes or 3 out 4 min were clinically detected as quiet sleep.In order to divide the data then into two distinct clusters, k-means clustering (k = 2) is performed using the smoothed temporal loading. So, each data point of the smoothed temporal signature of interest et sleep ,33.The effect of the smoothing and the result of the clustering is illustrated in The performance of the algorithm is evaluated using the annotations by expert clinicians. The sensitivity, specificity and accuracy will be reported. Moreover, to investigate the performance without the k-means clustering, receiver operating characteristic (ROC) curves are constructed based on the smoothed temporal signatures and the clinical labels. The area under the ROC curve (AUC) will be reported. Finally, Cohen’s kappa will be presented as well.Statistical analysis is performed to gain insight in the choice of the parameters. A statistical test is used to investigate whether the area under the absolute value of the autocorrelation function is a good feature to detect the component of interest. In order to do this, the agreement between each of the R temporal signatures obtained from a rank-R CPD and the clinical sleep labels are evaluated using the Kappa score. The temporal signatures are then sorted in descending order based on their Kappa statistic. As a result, the first component is the most related to sleep staging and should have the largest area under the absolute value of its autocorrelation function (ACF). A statistical test is then performed to assess whether the area under the ACF of the component of interest is significantly different from the other components. In addition, a statistical test is carried out to investigate the influence of the rank R on the performance. More specifically, we compared Cohen’s Kappa between models with different values of the rank R. In all analyses, the Shapiro-Wilk test is used to test for normality. If the data is normally distributed, one-way ANOVA is used, otherwise a Kruskal-Wallis test is performed. The significance level is always set equal to 0.05.Comparison of the factor matrices obtained using the rank indicated by CORCONDIA or DIFFIT with the ones for other ranks revealed that the diagnostics were often not suitable to define the appropriate number of components in this application. Therefore, the rank has not been fixed for each recording, instead the results are reported for different values of the rank R. Based on these findings we decided to also report the results for an age-dependent rank, where the rank is chosen equal to 1 for preterm recordings and equal to 2 for EEGs recorded from 37 weeks PMA onwards. The performance of this approach is set out in the sixth row of Finally, the last two rows of the table show the classification performance when the optimal rank (between 1 and 5) for each recording was selected based on Cohen’s kappa computed with the ground truth annotations. Specifically, the last row corresponds to the highest attainable performance , while in the penultimate row the optimal rank was used but the component of interest was automatically selected using the strategy based on the autocorrelation explained above. As a consequence, the difference between these two rows is caused by failures of the automatic component selection procedure.As explained in This study aimed to discriminate quiet sleep (QS) from non-quiet sleep (NQS) using the nonlinear dynamics of the EEG signal in a data-driven way. The proposed algorithm relies on the fact that the EEG complexity is different depending on the sleep state. In order to quantify the complexity of the EEG signal, multiscale entropy is computed for consecutive segments of a multichannel EEG recording. These multiscale entropy values are then used to construct a third-order tensor with modes channels, scales and segments. Subsequently, a rank-R nonnegative CPD of the multiscale entropy tensor is computed. The temporal signature of interest is detected based on the area under the absolute value of its autocorrelation function. After smoothing, this temporal signature shows a cyclic pattern reflecting the neonatal sleep staging. Clustering is then performed to discriminate quiet sleep from non-quiet sleep.The performance of the algorithm is reported for different ranks. The average Kappa is equal to 0.47 and 0.49, for a rank-1 and rank-2 CPD, respectively. However, if a rank-1 decomposition is used for EEGs recorded at a postmenstrual age below 37 weeks and a rank-2 decomposition is used for EEGs recorded at an older age, a Kappa of 0.53 is obtained. This indicates that a higher rank is preferred for neonates at term equivalent age, while rank 1 is suitable for most of the preterm recordings. The fact that for some recordings rank 1 is not sufficient, is demonstrated by the example in There are various possible explanations for the need for a higher rank for recordings at term-equivalent age. First of all, it is likely that this is due to the increased amount of artifacts in near term EEG recordings. It is expected that an older neonate will move more, which could lead to more severe distortions and lower quality of the EEG signal. The higher performance for higher ranks in noisy preterm EEG recordings confirms this reasoning. Secondly, the emergence of four distinct sleep states around 36 weeks PMA might also play a role in this phenomenon .The performance of the described algorithm is lower compared to state-of-the-art algorithms ,14,34. Hr and thus more template matches and consequently lower entropy. Detecting artifacts as quiet sleep periods will lead to a reduced performance, since the majority of the (motion) artifacts occur during non-quiet sleep. The smoothing operation is implemented to reduce the influence of artifacts and can deal with short duration artifacts (sudden drop in EEG complexity). Yet, the postprocessing cannot avoid false detections caused by longer artifacts (on multiple channels). Further research might explore incorporating information about the noise in the algorithm to make it more robust to artifacts. Besides, an estimation of the amount of noise could be used to get an initial estimate of the proper number of components. Notwithstanding these limitations, the study suggests that a decomposition of a multiscale entropy tensor can be used to discriminate neonatal sleep stages.The main weakness of this study is the lack of a method to define the rank for each new EEG recording automatically in a data-driven way. This is a general issue in tensor applications, therefore it is suggested to combine different tools to asses the rank ,37. We tThis study is an exploratory analysis on the use of tensor decompositions for sleep stage identification in preterm infants. Only the differences in complexity between different sleep stages are used. However, in future work the temporal signature could be combined with other discriminating features  to boost the performance. Moreover, as explained before, the heart rate variability, respiration and body movements of the infant are also dependent on the sleep state. Hence, in future research the combination of these complementary modalities can be studied. Finally, other tensorization techniques can be examined in future investigations.This study confirmed that the complexity of brain dynamics exhibits fundamental differences between vigilance states in preterm newborns. The EEG complexity is significantly lower during quiet sleep compared to non-quiet sleep. This property is exploited to develop an unsupervised algorithm that can detect quiet sleep in preterm infants based on the data-driven factorization of the multiscale entropy tensor.


In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement. This shifting paradigm created considerable challenges for the interpretation of large datasets and the generation of meaningful outcomes impacting process and product attributes. Among the numerous computational methods developed in this context, metabolic modeling tools allowed the coherent organization of large datasets into biological networks providing nonintuitive insights on biological systems that in vivo experiments alone cannot provide2. These model-based approaches have been proven to be invaluable at the level of preclinical research. For example, these approaches have been used for the design of new drugs by informing target selection and for the engineering of cells by rewiring metabolism towards the production of a product of interest4.The emergence of high-throughput technologies is elevating the biotechnology field to the era of big data8. However, model-based methods can be used for much more than this. For instance, these methods could also be applied at the industrial level in the field of process design, monitoring and control to lower the experimental effort and increase the process robustness. Indeed, model-based methods would allow a more rational design of bioprocesses but also the objective identification of the variables to monitor and control. Therefore, the routine implementation of these methods can be expected to greatly facilitate the implementation of regulatory requirements related to the Quality by Design paradigm  and Process Analytical Technologies initiative . Numerous modeling methods exist to describe and quantify the reactions occurring in a cell under specific environments, from deterministic kinetic models to stochastic and statistical models. While kinetic modeling has been widely used for small-scale metabolic models, these mechanistic models are for the moment not scalable and suitable for genome-wide approaches due to challenges in acquiring the required kinetic parameters  and computational problems associated with such complex parametric systems 10. On the other hand, while statistical modeling is very useful for the manipulation of large complex datasets, these black-box approaches have limited capacity to generate biologically relevant information for bioprocess improvement. Therefore, the lack of clear workflows to set up specific process models based on genome-wide information have limited the potential scope of computational biology applications in industry.Pharmaceutical companies are already investing substantially in computational approaches to guide drug discovery and cell design. While these important applications are out of the scope of this paper, we refer the interested readers to detailed reviews on the usage of metabolic engineering and synthetic biology in industrial context13, their practical implementation in biopharmaceutical research and development facilities still seems in its infancy. Actually, bioprocess improvement performance is still mainly achieved by semi-empirical media and bioprocess optimization 14. In this paper, gaps in current knowledge and technical limitations restraining the widespread usage of metabolic modeling tools for process development  in biopharmaceutical industry are highlighted. Specifically, research topics in three fields  are identified as key drivers for evolving the current state of some systems biology tools in industrial process engineering applications. Advances in these fields will be invaluable to address existing challenges in bioprocess development and improvement.However, the systems biology field is in continuous development and genome-scale metabolic networks are now available for most of the industrially relevant organisms. Therefore, we should be able to systematically predict cell metabolic behavior under different specific complex media compositions and process conditions. These types of predictions should be used to objectively determine the optimal operating conditions with respect to the desired cell phenotype and related production criteria. Unfortunately, while numerous studies have successfully used genome-scale models to rationally guide the culture process design16.Real-time monitoring is a key issue for effective bioprocess operation. Indeed, one needs first to recognize and analyze cellular physiological state in function of environmental conditions to be further able to adequately manipulate operating conditions. Therefore, real-time monitoring technologies development is a critical aspect of the bioprocess automatization. Previously, only a small number of so-called “process variables” were commonly measured online in bioreactors . The scope of available real-time probes has now been significantly expanded thanks to the recent advances in analytical technologies and the adoption of Process Analytical Technologies initiative17. Unfortunately, some of these experimental measurements cannot currently be achieved online in situ and, doing so, limit our capacity to have a live snapshot of the cell metabolism during the culture process. However, continuous improvements in analytical technologies and the emergence of the single-cell experiments open new perspectives for gaining insights into cell metabolism.In the context of bioprocesses, real-time measurements of the metabolites and cell concentrations in the cell culture medium are key elements to enable the prediction of cell phenotype with respect to operating conditions 19. However, the capacity to systematically extract accurate quantitative data is, for the moment, limited to a handful number of metabolites . We refer the reader to the publications of Li et al.20 and Ryder21 for detailed comparison of the performance and limitations of existing spectroscopic methods. In this context, chemometric modeling has been a state-of-art technique to effectively extract the maximum of information from spectroscopic spectra. It is worth to highlight that spectroscopic sensors can in this sense be embedded in the concept of soft sensor22 as they are used to compensate the lack of specific measurement by reconstructing the missing signals based on available measurements using data analysis models23. This type of hybrid approach will therefore continue to greatly benefit from the flourishing evolution of artificial intelligence tools and certainly allows to overcome the current limitations of spectroscopic monitoring methods24.Spectroscopic methods  have attracted an increasing interest for their potential capacity of simultaneous in situ measurement of nutrients, metabolites and cell concentrations. These techniques have been successfully integrated into real-time pharmaceutical manufacturing to provide a “molecular fingerprint” of samples enabling in-process correction needed to ensure the acquisition of a specific quality product25. These “single-cell approaches” in combination with adequate analytical methods could help unravel the impact of operating conditions on the cellular physiology. Specifically, the new generation of high-throughput-omics technologies allow to envision in a close future a near real-time measurements of cell transcriptome and proteome28. Interestingly, very few examples of interfacing flow cytometry with a bioreactor and its use for effective bioreactor control can be found in the literature29.Parallelly, the rise of online single‐cell probing opens the door for the specific investigation of cell metabolism and bioenergetics at a definition level never captured before12, the complexity of these large networks also hinders their utility in various practical applications. Actually, metabolic networks involving, most of the time, more reactions than metabolites, the associated system presents, generally, a multitude of solutions . Therefore, metabolic network structures are also too complex to be handled for the development of utilitarian tools .A genome-scale metabolic model is a network connecting all metabolic reactions that can occur in a specific organism with their associated metabolites, proteins and genes. While these models have been proven to be powerful tools for in silico simulation of cell metabolism30 or Ataman et al.31 allow an easy-to-implement systematic reduction of genome-scale networks into core models. However, these methods rely on the definition by the user of the part of the model that should be “protected” during the reduction  which is far from easy task. On the other hand, numerous algorithms have been developed to tailor genome-scale metabolic networks by integrating diverse types of -omics data, including transcriptomics, proteomics and metabolomics35. Actually, genome-scale models are used to link genes, enzymes, and metabolism through the use of gene-protein-reactions (GPR). Therefore, the knowledge of GPR is used by researchers to explain the metabolic state of a cell, based on the expression of metabolic genes and/or specific proteins37. The most common “integration algorithms” use gene expression data to recapitulate the metabolism of an organism under a specific condition  by only extracting the subset of active reactions from the genome-scale model. These methods have been proven to be valuable for enhancing the accuracy of model-predicted growth rates and gene essentiality37. However, since currently no quantitative description of the GPR relationship exists in genome-scale metabolic models, the integration of gene expression data requires the use of strong assumptions to link the GPR expression and the metabolic reaction activity, which could lead to an oversimplification of the complex relation existing between fluxes, enzymes and genes38.Different approaches have been proposed for tailoring these metabolic networks based on a priori knowledge and/or available experimental data. For example, algorithms like the one presented in Erdrich et al.39) will always be required to achieve an instantaneous picture of the flux distributions in the cell. A recent research in this field presented a new approach using biochemical thermodynamic constraints  to shape the solution space of potential flux distributions40. The promising results obtained in this study pave a new way towards the understanding of the mechanisms that governs the definition of metabolic pathway usage across organisms and conditions. Further development of these computational methods will be crucial to obtain a holistic and integrated picture of the cell metabolism in function of specific environmental conditions while considering uncertainty associated with experiments, measurements and modeling.Finally, it is important to highlight that these “network tailoring” approaches generally do not completely leverage the problem of system underdetermination related to network complexity. Therefore, the choice of adequate strategy to solve the system  with data-driven methods to describe the unknown parts. This type of models provides an attractive method for modeling biochemical processes as much of the underlying complexity can be lumped in the data-driven part. Therefore, these approaches can be used to effectively establish the link between metabolism and operating conditions in a way that can be used to implement control and optimization strategies.46, the first application of hybrid modeling in systems biology only arose in 201047 and few have been reported ever since53. One reason might be the absence of dedicated software that would allow for a straightforward development of these type of models. Another reason might be the broad interdisciplinarity of competences required to implement these approaches . Finally, there is still several major open questions that need to be investigated to allow a better combined usage of hybrid modeling and metabolic modeling tools54. From the effective generation of sufficiently informative experimental data to the joint parameter identification of the mechanistic and data-driven parts of the model, the assessment of these gaps will be critical for the development of a systematic workflow for integrating knowledge from systems biology into model structures enabling a next-generation technology for the life-sciences and biotechnology sectors, besides others .Process intensification through continuous processing has been adopted for decades in industries such as chemical and petrochemical. In the context of biopharmaceutical production, the move from batch to continuous or semi-continuous mode of production has been slowed down mainly due to the more conservative approach of this industry resulting from the stringent quality/regulatory requirements. Therefore, innovation in biotechnology has become more product- than process-centered57.The proper implementation of continuous manufacturing requires a thorough understanding of the process to achieve and maintain steady-state conditions . Furthermore, the in-process quality should be evaluated and ensured by the real-time analysis of process data. To this end, in-line process monitoring system linked to robust model-based control will be crucial technologies to support this real-time release testing14.Despite their advantages, the potential of metabolic modeling tools is still not fully exploited to address these challenges in bioprocess development and improvement. This is mainly due to the upfront time and effort investment required to implement these approaches and gain in process understanding as critical gaps remain to set these methods up in clear workflows. Empirical process development techniques will continue to play an important role in addressing these challenges. However, systems biology-driven approaches are likely to be more impactful to provide insights into the cellular responses to process conditions changes59. In this context, computational tools will be invaluable to systematically identify system limitations and areas of improvement for production efficiency, as demonstrated in the work of Vilkhovoy et al.60.Finally, the research topics, highlighted in this review, in the field of monitoring, network science and hybrid modeling, will also be cutting-edge technologies to improve new emergent biopharmaceutical production platforms such as cell-free systems. Actually, cell-free protein synthesis has been emerging as a flexible and powerful platform to address challenges in biomanufacturing by overcoming inherent limitations related to the use of living cells. Cell-free systems offer the ability to design metabolic pathways towards the production of desired products but also to synthesize complex proteins with unnatural amino acids and to buildup artificial cells. Despite these promising features, challenges remain such as the proper control of post-translational modifications and the expansion of the genetic code for unnatural amino acids incorporation. To address these challenges, targeted gene editing and addition of adequate exogenous substances to control reaction conditions will be needed to optimally regulate the transcription and translation4. Taken together, these technologies could facilitate the switch to continuous processing in biopharmaceutical industry but also foresee needs for improvements in emergent biomanufacturing platforms such as cell-free systems.We highlighted gaps in our knowledge and technical limitations restraining the effective application of metabolic modeling tools throughout the lifecycle of a biopharmaceutical process. Specifically, advances in real-time monitoring of bioprocesses, biological network modeling and their combination with data-driven approaches will be the key drivers to lower the time and cost associated with the development of new drugsReporting Sum


The incidence of adenocarcinoma of the esophagus (EAC) is increasing worldwide. In some regions of Europe, more adenocarcinomas are now treated than squamous cell carcinomas. In the Asian region, squamous cell carcinoma is clearly dominant, but adenocarcinoma is also diagnosed there more often ,2.Neoadjuvant therapy regimens, such as the CROSS scheme, have slightly improved the prognosis in potentially curatively-treatable patients over the last 10 years. Especially patients with adenocarcinoma benefit less significantly compared to those with squamous cell carcinoma. In the case of recurrence, hardly any personalized therapy options are available and unfortunately, only for a part of the patients is a curatively-intended therapy possible at all when the diagnosis is made. The choice of appropriate palliative therapeutics or other palliative measures is of high practical relevance .At the molecular and therapeutic level, there are similarities and relevant differences between the adenocarcinoma of the stomach and the adenocarcinoma of the esophagus. While the adenocarcinoma of the stomach is molecularly divided into four subtypes , Microsatellite instable (MSI), Epstein-Barr-Virus-related (EBV), Genomic stable (GS)), the adenocarcinoma of the esophagus shows, almost exclusively, the molecular characteristics of the CIN subtype, which is characterized by a TP53 mutation and a high number of copy number variations (including amplifications of ERBB2 and KRAS). In contrast to the stomach, the MSI subtype is very rarely found in the esophagus (20% versus 1%) and the EBV subtype not at all. The two latter subtypes are relevant with regard to a therapy prediction of immune checkpoint inhibitors (ICI). The molecular characteristics of the approximately 10% ICI-sensitive EACs are currently being analyzed .One of the peculiarities is the fact that 70–90% of EACs occur in men. It is completely unclear why it is predominantly men who develop the carcinoma, while the main risk factor, Barrett’s mucosa, occurs only twice as frequently at most in men. What factors promote the malignant transformation of Barrett’s mucosa in men ,6?How can I use newly gained molecular insights to successfully apply personalized therapeutics for defined subgroups? What is the composition of the inflammatory micromileu of EAC? Are there definable subgroups beyond PD-L1 expression that particularly benefit from immunocheckpoint blockade ? When isThis Special Issue on Current and Future Treatment Strategies for Esophageal Adenocarcinoma intends to bring together the current state of knowledge of EAC, to look beyond the horizon and to raise relevant questions that need to be solved in the future. Both basic scientists and clinicians will have their say.


In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside the thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers that are placed on the surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of the human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of the thoracoabdominal torso. Point cloud data from depth cameras were used to achieve three-dimensional modeling of the chest and abdomen surface during respiration, and a dimensionality reduction algorithm was proposed to extract respiratory features from the established voxel model. Finally, experimental results including the accuracy of voxel model and correlation coefficient were compared to validate the feasibility of the proposed method, which provides enhanced accuracy of target motion correlation than traditional methods that utilized external markers. Therefore, correlating the skin motion with tumor motion simultaneously caused by respiration is necessary before establishing a prediction model to obtain the upcoming position of tumor.Robotic radiosurgery shows significant advantages in treating tumors that are not suitable to be treated by chemotherapy  features, and motion measurement of the chest and abdomen surface, respectively.in vivo, but insufficient characterization in experiments was found  superpixels and obtained the feature embedding space of each superpixel, from which the superpixel feature vector was extracted  establish dynamic thoracoabdominal surface voxel model, (2) reduce the dimension of the voxel model and extract the low-dimensional representation vector of the voxel model, and (3) establish the correlation model between the representation vector and the tumor motion state.Three-dimensional modeling of dynamic human thoracoabdominal skin surface during respiration mainly includes point cloud collecting of dynamic skin surface using multiple cameras of Kinect V2, model establishment of thoracoabdominal skin surface, and surface reconstruction into voxel model.With modeling integrity considered, we adopted time-series-based strategy when building the model with data from multiple cameras. Simultaneous frames of different cameras were fused into one frame, and then, the fused frames were arranged in time sequences. Due to the overlap in the fusion of two frames of time asynchrony, it is necessary to consider the time synchronization problem of multicamera sampling.Since the exposure and trigger times of multiple cameras cannot be completely unified, we considered sampling with an approximate synchronization strategy. Two computers control two cameras in a one-to-one manner.xs to the calibration plate coordinate system as xm was as follows:As images from multiple cameras have their own coordinates, an algorithm based on 2D image calibration was developed to unify the separate coordinates. In the algorithm, a calibration plate coordinate was built first by identification of the corner points, and then, the camera coordinate could be converted to the universal coordinates. Transformation from the camera coordinates system at any point ts is the center of the calibration plate in the camera coordinate system, and Rs is the rotation matrix. The position of the calibration plate in the world coordinate system is known, so any point can be converted from the coordinate system xm to the world coordinate system xc:Among them, Rm and tm are the rotation and translation of the calibration plate in the world coordinate system. where Although the point clouds of multiple cameras have been unified into the same coordinate system, the raw data of point cloud has noises and outliers brought by cameras themselves and infrared interference between each other camera. To pre-process the raw data, we used bilateral filtering  in the target point cloud P and the source point cloud Q according to certain constraint conditions and then calculate the optimal matching parameter R and t to minimize the error function E. The error function is:Due to certain calibration errors, the registration of the adjusted multiple point clouds still has overlap, which requires precise registration. We used a classical point cloud registration algorithm iterative closest point (ICP) to register multiple point clouds. The basic principle of ICP algorithm is to find the nearest point  was built by projecting the corresponding points qi ∈ Q in the original point cloud Q to the plane PL. To make the model watertight, boundary insertion was executed by using a method based on projection interpolation. As has been described above in S(t) and the end of the abdomen E(t) was obtained by interpolating point cloud equally along the z negative axis to the plane PL, respectively; the waist part W(t) was obtained by interpolating point cloud equally along the waist curvature to the plane PL.The watertight model composed of point cloud expressed the 3D information of the measured surface by independent points. To illustrate the space relationship of the entire surface, meshing was made to the point cloud model. Here, we used the classical Poisson reconstruction method , it is unrealistic to directly use these data to correlate with tumor motion because of a lot of noise and redundant information in huge data. In order to more effectively correlate with tumor motion and fully reflect surface motion characteristics, we first extract its physical significance features, then perform data analysis on the voxel model to extract respiratory features.The movement of breathing is accompanied by the heaving of the chest and abdomen. Intuitively speaking, the expansion and contraction of the chest and abdomen must cause the changes of the entire volume V and surface area S of thoracoabdominal trunk.The entire voxel model's voxel number variation with time series reflects the volume change of the thoracoabdominal trunk. To study the respiration characteristic displayed by volume changes, the voxel units of voxel model of each frame were traversed, and then, the volume of voxel units was accumulated; therefore, the volume change is reflected in the time series. The area change of voxel model is reflected in the area of voxel's outer layer in the similar way.Intrinsic data characteristics refer to the information that reveals the motional information of the body surface obtained by the voxel model. It is obtained by reducing the dimensionality of the voxel model. It is necessary for the construction of the respiration tracking model.Just as point cloud generation came with time stamps, the state of voxel model varied over time series. Each frame of the voxel model stood for a respiratory state, which was expressed by probability of voxel occupancy. The overview of the proposed method was presented in Algorithm 1.Depending on the voxels' occupation and idleness, the state of voxels in the template-bounding box were marked with probability 1, 0, respectively. Thus, the voxel model state varied by time was expressed by the marker value of each voxel in the template-bounding box, as shown in the left diagram in V, S and essential parameters ψ = (m ≤ d + 1) after data analysis, characteristic variables Γ that can express time-varied states of the voxel model can be obtained as:Through combining physical variables After feature extraction of voxel model in previous section, we obtained the representation vector of the whole thoracoabdominal surface motion. The establishment of respiratory correlation model is a key part of tumor tracking. The correlation function is to take the breathing surrogate signal  as the input data and realize the motion estimation of the internal tumor by correlating the surrogate with the movement of the internal tumor. Therefore, we used the extracted representation vector as external surface motion surrogate to establish a correlation model with internal tumor motion.Tumor movement is complex since it is caused by various factors such as respiration, heartbeat, and abdominal pressure. During inhalation, the volume of air inhaled by the lungs becomes larger because of the thoracoabdominal cavity and the diaphragm deform. During the exhalation process, the volume of gas remaining in the lungs becomes smaller; at the same time, the thoracoabdominal cavity and diaphragm are restored to their original state. During normal breathing, at the same transpulmonary pressure, the expiratory volume is greater than the inspiratory volume. The so-called hysteresis phenomenon (the phase lag between body surface characteristic respiratory motion and tumor respiratory motion) is attributed to the complex respiratory pressure–volume relationship of the lungs and chest and abdomen.Therefore, exhalation and inhalation are two irreversible motion states. Before establishing the correlation model, we need to divide the entire breathing process into exhalation part and inhalation part and model these two processes, respectively. This paper divides the respiratory phase according to the amplitude of the external motion and uses the peak and valley values of the motion as the dividing points of exhalation and inspiration.in vivo) and external motion (the motion information of body surface), we adopt a polynomial model  depth cameras placed at both sides of the experimental bed to collect data. Because the view of a single camera is limited, blind areas cannot be observed on the abdomen exit and will prevent a successful 3D modeling of thoracic–abdominal surface. However, using two Kinect V2 cameras has a complete view of the patient's body and is conducive to building the whole thoracic–abdominal model. The experimental subject is a phantom developed in our lab  is equal to the covariance cov between them divided by the product of their respective standard deviations . The value of the coefficient is always between −1.0 and 1.0. Variables close to 0 are regarded as uncorrelated, while variables close to 1 or −1 are regarded as strongly correlated.where the Pearson correlation coefficient ρin vivo is carried out using Equation (6). As shown in in vivo. The correlation coefficient between this only dimension and tumor in vivo reached more than 0.9. Therefore, we can conclude that reducing the superhigh-dimensional voxel model to 1 dimension is the optimal operation.Three sets of phantom data were, respectively, used to build voxel models and were reduced into characteristic vectors with 10 dimensions first. Then, correlation analysis between each dimension and tumor movement In order to verify the correlation between each variable and tumor movement, we also calculated the correlation coefficient between tumor motion, dimension-reduced data, and NDI markers, respectively, by using Pearson method as Equation (6). The results are in in vivo.As the correlation model has been introduced in the previous section, one-dimensional data were obtained by the dimension reduction method to establish the correlation model with tumor motion in vivo using skin surface motion, while the feasibility and accuracy of the method were proved by comparing the error between the actual tumor motion and the estimated value.The obtained data (last for 1 min) is divide into two parts: one part was used to establish the correlation model, and the other one was used to verify the accuracy of the correlation model. That is, the correlation model was used to estimate the tumor motion Then, we established the correlation model with tumor motion by using the dimensionality reduction results and NDI marker positions, respectively. After that, correlation models were used to estimate tumor motion. Three sets of phantom data with point cloud collection and NDI maker positions were used to test and compare the two correlation models. In this paper, a novel characterization method of human external respiratory motion for tumor correlation was proposed. Compared with previous methods of limited markers and multiple modality surrogates, our method accomplished better characterization by establishing a 3D voxel model containing rich structural information of thoracic and abdominal torso during the respiratory motion. Dimensions of the voxel model were reduced by a dimensionality reduction method. Through these procedures, features that can reflect changes in external respiratory motion were obtained. This paper builds the model of chest and abdomen torso's respiratory motion, from multicamera system construction, point cloud processing, and watertight model construction to model voxelization. A dimensionality reduction method was proposed to extract respiratory features from the established voxel model. The feasibility of the characterization method based on chest and abdomen surface modeling was verified and compared with the traditional finite surface marker representation method. The proposed method was proved to be more accurate than the traditional one with limited external markers. Furthermore, it breaks through the limitation of information loss in current respiration correlation methods, which describe the respiratory motion by using sparse sensing data. In future work, to form a sound solution of respiration tracking for radiosurgical robots, we will optimize the process of thoracoabdominal skin surface modeling, study the feature extraction of voxel model, and apply this method to the prediction of tumor motion under the clinical application of tumor tracking.All datasets generated for this study are included in the article/supplementary material.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.SY contributed to the manuscript writing, data analysis, and experiment design. PH contributed to the manuscript writing and data analysis. RS conceived the presented idea and contributed to the algorithm design. MZ and JG contributed to the manuscript revision and system design. FZ, SK, and LS contributed to the support of experimental equipment. All authors discussed the results and contributed to the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.