Search

This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect and track pedestrians, benefiting from LiDAR’s capability to directly provide accurate 3D data. After LiDAR-based detection, the approach leverages the typically higher data density provided by 2D cameras to determine the body pose of the detected pedestrians. The approach combines several state-of-the-art machine learning techniques: it uses a neural network and a subsequent voting process to detect pedestrians in LiDAR sensor data. Based on the known geometric constellation of the different sensors and the knowledge of the intrinsic parameters of the cameras, image sections are generated with the respective regions of interest showing only the detected pedestrians. These image sections are then processed with a method for image-based human pose estimation to determine keypoints for different body parts. These keypoints are finally projected from 2D image coordinates to 3D world coordinates using the assignment of the original LiDAR points to a particular pedestrian.

Path planning for a measuring vehicle requires solving two popular problems from computer science, namely the search for the optimal tour and the search for the optimal viewpoint. Combining both problems results in a new variation of the Traveling Salesman Problem, which we refer to as the Explorational Traveling Salesman Problem. The solution to this problem is the optimal tour with a minimum of observations. In this paper, we formulate the basic problem, discuss it in context of the existing literature and present an iterative solution algorithm. We demonstrate how the method can be applied directly to LiDAR data using an occupancy grid. The ability of our algorithm to generate suitably efficient tours is verified based on two synthetic benchmark datasets, utilizing a ground truth determined by an exhaustive search.

Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

This paper presents and extends an approach for the detection of pedestrians in unstructured point clouds resulting from single MLS (mobile laser scanning) scans. The approach is based on a neural network and a subsequent voting process. The neural network processes point clouds subdivided into local point neighborhoods. The member points of these neighborhoods are directly processed by the network, hence a conversion in a structured representation of the data is not needed. The network also uses meta information of the neighborhoods themselves to improve the results, like their distance to the ground plane. It decides if the neighborhood is part of an object of interest and estimates the center of said object. This information is then used in a voting process. By searching for maxima in the voting space, the discrimination between an actual object and incorrectly classified neighborhoods is made. Since a single labeled object can be subdivided into multiple local neighborhoods, we are able to train the neural network with comparatively low amounts of labeled data. Considerations are made to deal with the varying and sparse point density that is typical for single MLS scans. We supplement the detection with a 3D tracking which, although straightforward, allows us to deal with objects which are occluded for short periods of time to improve the quality of the results. Overall, our approach performs reasonably well for the detection and tracking of pedestrians in single MLS scans as long as the local point density is not too low. Given the LiDAR sensor we used, this is the case up to distances of 22 m.

Recording an ever-changing urban environment in a structured manner requires sensor deployment planning. In case of mobile sensor platforms, this also includes verifying the terrain navigability. Solving both tasks would usually require different application-specific data structures and tools. In this work, we propose a theoretical framework that provides a uniform representation for spatial information as well as the tools required to combine, manipulate and visualize it. We provide an efficient implementation of the framework utilizing octree-based evidence grids. Our approach can be used to solve complex tasks by combining simple spatial information sources, which we demonstrate by providing simple solutions to the aforementioned applications. Despite the use of a volumetric approach, our runtimes are within the range of minutes.

This paper presents an approach which uses a PointNet-like neural network to detect objects of certain types in MLS point clouds. In our case, it is used for the detection of pedestrians, but the approach can easily be adapted to other object classes. In the first step, we process local point neighborhoods with the neural network to determine a descriptive feature. This is then further processed to generate two outputs of the network. The first output classifies the neighborhood and determines if it is part of an object of interest. If this is the case, the second output determines where it is located in relation to the object center. This regression output allows us to use a voting process for the actual object detection. This processing step is inspired by approaches based on implicit shape models (ISM). It is able to deal with a certain amount of incorrectly classified neighborhoods, since it combines the results of multiple neighborhoods for the detection of an object. A benefit of our approach as compared to other machine learning methods is its low demand for training data. In our experiments, we achieved a promising detection performance even with less than 1000 training examples.

This paper proposes a change detection approach that uses a low-resolution octree enhanced with Gaussian kernels to describe free and occupied space. This so-called Gaussian Occupancy Octree is derived from range measurements and used to represent spatial information for a single epoch. Changes between epochs are encoded using a Delta Octree. A qualitative and quantitative evaluation of the proposed approach shows that its advantages are a fast runtime and the ability to make a statement about the re-exploration of space. An evaluation of the classification accuracy shows that our approach tents towards correct classifications with an overall accuracy of 51.5 %, but is also systematically biased towards the appearance of occupied space.

In this paper we present a versatile multi-sensor vehicle which is used in several research projects. The vehicle is equipped with various sensors in order to cover the needs of different research projects in the area of object detection and tracking, mobile mapping and change detection. We show an example for the capabilities of this vehicle by presenting camera- and LiDAR-based pedestrian detection methods. Besides this specific use case, we provide a more general in-depth description of the vehicle’s hard- and software design and its data-processing capabilities. The vehicle can be used as a sensor carrier for mobile mapping, but it also offers hardware and software components to allow for an adaptable onboard processing. This enables the development and testing of methods related to real-time applications or high-level driver assistance functions. The vehicle’s hardware and software layout result from several years of experience, and our lessons learned can help other researchers set up their own experimental platform.

The focus of this paper is the processing of data from multiple LiDAR (light detection and ranging) sensors for the purpose of detecting persons in that data. Many LiDAR sensors (e.g., laser scanners) use a rotating scan head, which makes it difficult to properly timesynchronize multiple of such LiDAR sensors. An improper synchronization between LiDAR sensors causes temporal distortion effects if their data are directly merged. A merging of data is desired, since it could increase the data density and the perceived area. For the usage in person and object detection tasks, we present an alternative which circumvents the problem by performing the merging of multi-sensor data in the voting space of a method that is based on Implicit Shape Models (ISM). Our approach already assumes that there exist some uncertainties in the voting space. Therefore it is robust against additional uncertainties induced by temporal distortions. Unlike many existing approaches for object detection in 3D data, our approach does not rely on a segmentation step in the data preprocessing. We show that our merging of multi-sensor information in voting space has its advantages in comparison to a direct data merging, especially in situations with a lot of distortion effects.

Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

In this paper we present an approach for the detection of persons in point clouds gathered by mobile laser scanning (MLS) systems. The approach consists of a preprocessing and the actual detection. The main task of the preprocessing is to reduce the amount of data which has to be processed by the detection. To fulfill this task, the preprocessing consists of ground removal, segmentation and several filters. The detection is based on an implicit shape models (ISM) approach which is an extension to bag-of-words approaches. For this detection method, it is sufficient to work with a small amount of training data. Although in this paper we focus on the detection of persons, our approach is able to detect multiple classes of objects in point clouds. Using a parameterization of the approach which offers a good compromise between detection and runtime performance, we are able to achieve a precision of 0.68 and a recall of 0.76 while having a average runtime of 370 ms per single scan rotation of the rotating head of a typical MLS sensor.

Data recorded by mobile LiDAR systems (MLS) can be used for the generation and refinement of city models or for the automatic detection of long-term changes in the public road space. Since for this task only static structures are of interest, all mobile objects need to be removed. This work presents a straightforward but powerful approach to remove the subclass of moving objects. A probabilistic volumetric representation is utilized to separate MLS measurements recorded by a Velodyne HDL-64E into mobile objects and static background. The method was subjected to a quantitative and a qualitative examination using multiple datasets recorded by a mobile mapping platform. The results show that depending on the chosen octree resolution 87-95% of the measurements are labeled correctly.

The generation of 3D city models is a very active field of research. Modeling environments as point clouds may be fast, but has disadvantages. These are easily solvable by using volumetric representations, especially when considering selective data acquisition, change detection and fast changing environments. Therefore, this paper proposes a framework for the volumetric modeling and visualization of large scale urban environments. Beside an architecture and the right mix of algorithms for the task, two compression strategies for volumetric models as well as a data quality based approach for the import of range measurements are proposed. The capabilities of the framework are shown on a mobile laser scanning dataset of the Technical University of Munich. Furthermore the loss of the compression techniques is evaluated and their memory consumption is compared to that of raw point clouds. The presented results show that generation, storage and real-time rendering of even large urban models are feasible, even with off-the-shelf hardware.

Due to the spread of economically priced laser scanning technology nowadays, especially in the field of topographic surveying and mapping, ever-growing amounts of data need to be handled. Depending on the requirements of the specific application, airborne, mobile or terrestrial laser scanners are commonly used. Since visualizing this flood of data is not feasible with classical approaches like raw point cloud rendering, real time decision making requires sophisticated solutions. In addition, the efficient storage and recovery of 3D measurements is a challenging task. Therefore we propose an approach for the intelligent storage of 3D point clouds using a spatial database. For a given region of interest, the database is queried for the data available. All resulting point clouds are fused in a model generation process, utilizing the fact that low density airborne measurements could be used to supplement higher density mobile or terrestrial laser scans. The octree based modeling approach divides and subdivides the world into cells of varying size and fits one plane per cell, once a specified amount of points is present. The resulting model exceeds the completeness and precision of every single data source and enables for real time visualization. This is especially supported by data compression ratios of about 90%.

The availability of 3D environment models enables many applications such as visualization, planning or simulation. With the use of current mobile laser scanners it is possible to map large areas in relatively short time. One of the emerging problems is to handle the resulting huge amount of data. We present a fast and adaptive approach to represent connected 3D points by surface patches while keeping fine structures untouched. Our approach results in a reasonable reduction of the data and, on the other hand, it preserves details of the captured scene. At all times during data acquisition and processing, the 3D points are organized in an octree with adaptive cell size for fast handling of the data. Cells of the octree are filled with points and split into subcells, if the points do not lie on one plane or are not evenly distributed on the plane. In order to generate a polygon model, each octree cell and its corresponding plane are intersected. As a main result, our approach allows the online generation of an expandable 3D model of controllable granularity. Experiments have been carried out using a sensor vehicle with two laser scanners at an urban test site. The results of the experiments show that the demanded compromise between data reduction and preservation of details can be reached.

Abstract Background To examine the pathological effect of a mesial temporal seizure onset zone (SOZ) on local and inter-regional response to faces in the amygdala and other structures of the temporal lobe. Methods Intracranial EEG data was obtained from the amygdala, hippocampus, fusiform gyrus and parahippocampal gyrus of nine patients with drug-refractory epilepsy during visual stimulation with faces and mosaics. We analyzed event-related potentials (ERP), gamma frequency power, phase-amplitude coupling and phase-slope-index and compared the results between patients with versus without a mesial temporal SOZ. Results In the amygdala and fusiform gyrus, faces triggered higher ERP amplitudes compared to mosaics in both patient groups and higher gamma power in patients without a mesial temporal SOZ. In the hippocampus, famous faces triggered higher gamma power for both groups combined but did not affect ERPs in either group. The differentiated ERP response to famous faces in the parahippocampal gyrus was more pronounced in patients without a mesial temporal SOZ. Phase-amplitude coupling and phase-slope-index results yielded bidirectional modulation between amygdala and fusiform gyrus, and predominately unidirectional modulation between parahippocampal gyrus and hippocampus. Conclusions A mesial temporal SOZ was associated with an impaired response to faces in the amygdala, fusiform gyrus and parahippocampal gyrus in our patients. Compared to this, the response to faces in the hippocampus was impaired in patients with, as well as without, a mesial temporal SOZ. Our results support existing evidence for face processing deficits in patients with a mesial temporal SOZ and suggest the pathological effect of a mesial temporal SOZ on the amygdala to play a pivotal role in this matter in particular.

Context: Staging and monitoring of pre-symptomatic type 1 diabetes includes the assessment for dysglycemia., Objective: To assess the ability of Continuous Glucose Monitoring (CGM) to differentiate between islet autoantibody-negative controls and early-stage type 1 diabetes and explore whether CGM classifiers predict progression to clinical diabetes., Research Design and Methods: Children and adolescents participating in public health screening for islet autoantibodies in Bavaria, Germany were invited to undergo CGM with Dexcom G6. In total, 118 participated and valid data was obtained from 97 (57 female; median age 10 [range 3-17] years), including 46 with stage 1, 18 with stage 2, and 33 with no islet autoantibodies., Results: Mean glucose during CGM in islet autoantibody-negative controls was high (median, 115.3 mg/dl) and varied substantially (IQR, 106.8-124.4). Eleven (33%) of the controls had more than 10% of glucose values above 140 mg/dl (TA140). Using thresholds corresponding to 100% specificity in controls, differences between controls and stage 1 and stage 2 were obtained for glucose standard deviation, TA140, TA160 and TA180. Elevations in any two of these parameters identified 12 (67%) with stage 2 and 9 (82%) of 11 participants who developed clinical diabetes within one year. However, there was marked variation within groups for all parameters and poor consistency observed in a second CGM performed in 18 participants., Conclusion: This study demonstrated the potential of integrating CGM into staging and monitoring of early-stage type 1 diabetes. However, substantial improvement in the precision of CGM is required for its application in routine monitoring practices., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.)

Different conveyor belt materials used by the meat and other food industries were compared, regarding their cleanability as bacterial reduction rates in relation to their surface topography. Eleven thermoplastic polymers, four stainless steels, and five aluminized nanostructured surfaces were investigated under laboratory conditions. Cleanings were conducted with water only, and with an alkaline foam detergent. Overall, scanning electron microscopy revealed remarkable differences in the surface topography of the tested surfaces. Water cleaning results showed that nanostructured aluminized surfaces achieved significantly higher cleanability rates compared to the eight thermoplastic surfaces, as well as the glass-bead blasted rough stainless steel. Thermoplastic surfaces showed overall low cleanability rates when cleaned with alkaline detergent, while stainless steel and nanoporous aluminum showed high variations. Overall, nanoporous aluminum showed promising results as it can be used to coat conveyor belts. However, compatibility with cleaning detergent and sensitivity to scratches must be further investigated. Overall, it can be concluded that cleanability is not only influenced by surface roughness, but also by the overall surface finish, scratches, and defects., Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05778-0., Competing Interests: Conflict of interestThe authors declare no conflicts of interests., (© The Author(s) 2023.)

Responsive biomaterials, tunable from the molecular to the macroscopic scale, are attractive for various applications in nanotechnology. Herein, a long polypeptide chain derived from the abundant serum protein human serum albumin is cross-linked by dynamic-coordinative iron(III)/catechol bonds. By tuning the binding stoichiometry and the pH, reversible intramolecular folding into polypeptide nanoparticles with controllable sizes is achieved. Moreover, upon varying the stoichiometry, intermolecular cross-links become predominant yielding smart and tunable macroscopic protein hydrogels. By adjusting the intra- and intermolecular interactions, biocompatible and biodegradable materials are formed with varying morphologies and dimensions covering several lengths scales featuring rapid gelation without toxic reagents, fast and autonomous self-healing, tunable mechanical properties, and high adaptability to local environmental conditions. Such material characteristics can be particularly attractive for tissue engineering approaches to recreate soft tissues matrices with highly customizable features in a fast and simple fashion., (© 2021 The Authors. Macromolecular Rapid Communications published by Wiley-VCH GmbH.)

The high perishability of fresh meat results in short sales and consumption periods, which can lead to high amounts of food waste, especially when a fixed best-before date is stated. Thus, the aim of this study was the development of a real-time dynamic shelf-life criterion (DSLC) for fresh pork filets based on a multi-model approach combining predictive microbiology and sensory modeling. Therefore, 647 samples of ma-packed pork loin were investigated in isothermal and non-isothermal storage trials. For the identification of the most suitable spoilage predictors, typical meat quality parameters (pH-value, color, texture, and sensory characteristics) as well as microbial contamination (total viable count, Pseudomonas spp., lactic acid bacteria, Brochothrix thermosphacta , Enterobacteriaceae) were analyzed at specific investigation points. Dynamic modeling was conducted using a combination of the modified Gompertz model (microbial data) or a linear approach (sensory data) and the Arrhenius model. Based on these models, a four-point scale grading system for the DSLC was developed to predict the product status and shelf-life as a function of temperature data in the supply chain. The applicability of the DSLC was validated in a pilot study under real chain conditions and showed an accurate real-time prediction of the product status.

Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Canine elbow dysplasia (CED) is a complex developmental skeletal disorder associated with a number of pathological conditions within the cubital joint. Because CED is a heritable disease, it is important to identify and remove the affected animals from breeding. The first objective of this study was to describe the prevalence of medial coronoid process disease (MCPD) without (MCD) or with (FMCP) fragmented medial coronoid process, osteochondrosis (OC) and/or osteochondritis dissecans (OCD), ununited anconeal process (UAP), radio-ulnar incongruence (INC R-U) and humero-ulnar incongruence (INC H-U) in dogs with the use of CT imaging. The second aim was to determine the influence of demographics on the prevalence of investigated pathologies in dogs with clinical evidence of elbow dysplasia., Results: In this retrospective study, CT data records of 169 dogs of different breeds presented to the small animal veterinary clinic from 2012 to 2018 were included. 69.23% of dogs diagnosed with CED were young (≤ 2 years old). The mean age of dogs presented with INC R-U was 1.68 ± 1.82 years, while in dogs without INC R-U the mean age was 2.64 ± 2.59 years. The mean age of dogs with INC H-U was 1.94 ± 2.06 years, while without INC H-U 3.29 ± 2.09 years. Labrador Retrievers, German Shepherd and Bernese Mountain dogs were most frequently presented with CED-associated lameness. In 122 dogs OA of varying severity was found., Conclusion: INC H-U, FMCP and MCD were among the most frequently found components of CED found in the present study. OCD and UAP were the least frequently diagnosed. Dogs presented with INC R-U and INC H-U were significantly younger than dogs without these CED components. Boxers, Dog de Bordeaux, American Staffordshire terriers and mixed-breed dogs were diagnosed later in life than the other breeds. OA of varying severity was found in 72.18% of dogs. Males accounted for more than 75% of the study population., (© 2021. The Author(s).)

We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a variety of optical sensors over the past few years, and contains hardware for complete raw data acquisition, georeferencing, real-time data analysis, and immediate visualization on in-car displays. We demonstrate the capabilities of MODISSA by giving a deeper insight into experiments with its specific configuration in the scope of three different applications. Other research groups can benefit from these experiences when setting up their own mobile sensor system, especially regarding the selection of hardware and software, the knowledge of possible sources of error, and the handling of the acquired sensor data.

Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our analysis protocol on the analysis of a genome-wide CRISPR screen in hTERT-RPE1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our analysis protocol is general and can be applied on any cell type and with different CRISPR enzymes., (© 2021 Walter de Gruyter GmbH, Berlin/Boston.)

Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171 T , Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030 T , Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a data pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meat-related pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5% and 97.5%, respectively., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Broad-spectrum antivirals are powerful weapons against dangerous viruses where no specific therapy exists, as in the case of the ongoing SARS-CoV-2 pandemic. We discovered that a lysine- and arginine-specific supramolecular ligand (CLR01) destroys enveloped viruses, including HIV, Ebola, and Zika virus, and remodels amyloid fibrils in semen that promote viral infection. Yet, it is unknown how CLR01 exerts these two distinct therapeutic activities. Here, we delineate a novel mechanism of antiviral activity by studying the activity of tweezer variants: the "phosphate tweezer" CLR01, a "carboxylate tweezer" CLR05, and a "phosphate clip" PC. Lysine complexation inside the tweezer cavity is needed to antagonize amyloidogenesis and is only achieved by CLR01. Importantly, CLR01 and CLR05 but not PC form closed inclusion complexes with lipid head groups of viral membranes, thereby altering lipid orientation and increasing surface tension. This process disrupts viral envelopes and diminishes infectivity but leaves cellular membranes intact. Consequently, CLR01 and CLR05 display broad antiviral activity against all enveloped viruses tested, including herpesviruses, Measles virus, influenza, and SARS-CoV-2. Based on our mechanistic insights, we potentiated the antiviral, membrane-disrupting activity of CLR01 by introducing aliphatic ester arms into each phosphate group to act as lipid anchors that promote membrane targeting. The most potent ester modifications harbored unbranched C4 units, which engendered tweezers that were approximately one order of magnitude more effective than CLR01 and nontoxic. Thus, we establish the mechanistic basis of viral envelope disruption by specific tweezers and establish a new class of potential broad-spectrum antivirals with enhanced activity.

Background: The case report describes a rare congenital anomaly, crossed fused renal ectopia (CFRE), with coexistence of two other abnormalities - atypical portal vein and coccygeal vertebrae malformation in a domestic cat. The concomitance of those 3 congenital defects has not been described previously., Case Presentation: An 8-month-old female, domestic cat suffering from chronic diarrhea was referred to the diagnostic imaging unit. The patient showed no other clinical symptoms. An abdominal ultrasonographic examination was performed in order to evaluate the condition of abdominal organs, particularly the gastrointestinal tract. The ultrasound examination showed an ectopic duplex kidney at instead of kidneys in their typical location. Computed tomography (CT) with angiographic phase and excretory urography was requested to evaluate the condition of the kidneys and ureters. The final diagnosis was CFRE, atypical portal vein and coccygeal deformation in an asymptomatic cat with no changes in renal function and normal blood parameters., Conclusions: Crossed fused renal ectopia is a rare congenital anomaly and is easily detectable by an abdominal ultrasonographic examination and CT, which allows more complete assessment of both anatomical relations and secretory function of the kidney. The occurrence of CFRE, abnormal portal vein and spinal malformation in a clinically healthy patient is the evidence that congenital malformations may simultaneously involve various, not directly related structures and systems, without significant influence on blood and urine parameters. Thus the most useful tool in the evaluation of the morphological and functional changes is the diagnostic imaging, especially contrast enhanced CT. Our results show that renal fusions should be considered in the differential diagnosis of caudal abdominal masses.

The aim of the study was to compare the effects of corneal healing in case of application of stem cells in various forms, in relation to the antibiotic-assisted procedures. Rabbits were divided into 4 groups in the first stage of the experiment. Group 0 (negative control group) was not subjected to any actions, which would cause damage to the cornea. The remaining three groups had their cornea damaged. Group 1 (positive control group) - no drugs were administered during the experiment. Rabbits in group 2 were administered with ointment containing stem cells to the lesion, while group 3 - with ofloxacinum. The stem cells were administered during the first five days, twice a day, onto the corneal surface. The further course of the experiment consisted of observing the rate of healing of the injured cornea and assessment of its transparency, size of lesion, hyperaemia, eyelid spasm and outflow from the conjunctival sac after 5, 10 and 20 days.In the second stage the animals were euthanised after clinical examination on the twentieth day of the experiment, in order to analyse the corneal reparative processes on the same day. The studies revealed that the application of antlerogenic stem cells had a positive effect on the healing process of corneal defects. The application thereof not only shortened the healing time, but also weakened or arrested the development of side effects. The results have demonstrated that the epithelial proliferation in each group was different. The longest was maintained in the group with stem cells, the shortest - in the group with chemotherapeutics. The use of antlerogenic stem cells had a positive effect on the healing process of corneal lesions. The use of stem cells helped to maintain high transparency of the cornea., (Copyright© by the Polish Academy of Sciences.)

In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types., Competing Interests: The authors have declared that no competing interests exist.

The development of a synthetic code that enables a sequence programmable feature like DNA represents a key aspect toward intelligent molecular systems. We developed herein the well-known dynamic covalent interaction between boronic acids (BAs) and catechols (CAs) into synthetic nucleobase analogs. Along a defined peptide backbone, BA or CA residues are arranged to enable sequence recognition to their complementary strand. Dynamic strand displacement and errors were elucidated thermodynamically to show that sequences are able to specifically select their partners. Unlike DNA, the pH dependency of BA/CA binding enables the dehybridization of complementary strands at pH 5.0. In addition, we demonstrate the sequence recognition at the macromolecular level by conjugating the cytochrome c protein to a complementary polyethylene glycol chain in a site-directed fashion.

Current technologies used to generate CRISPR/Cas gene perturbation reagents are labor intense and require multiple ligation and cloning steps. Furthermore, increasing gRNA sequence diversity negatively affects gRNA distribution, leading to libraries of heterogeneous quality. Here, we present a rapid and cloning-free mutagenesis technology that can efficiently generate covalently-closed-circular-synthesized (3Cs) CRISPR/Cas gRNA reagents and that uncouples sequence diversity from sequence distribution. We demonstrate the fidelity and performance of 3Cs reagents by tailored targeting of all human deubiquitinating enzymes (DUBs) and identify their essentiality for cell fitness. To explore high-content screening, we aimed to generate the largest up-to-date gRNA library that can be used to interrogate the coding and noncoding human genome and simultaneously to identify genes, predicted promoter flanking regions, transcription factors and CTCF binding sites that are linked to doxorubicin resistance. Our 3Cs technology enables fast and robust generation of bias-free gene perturbation libraries with yet unmatched diversities and should be considered an alternative to established technologies., Competing Interests: MW The Goethe University Frankfurt has filed a patent related to this work on which MW is an inventor (WO2017EP84625), VD The Goethe University Frankfurt has filed a patent related to this work on which VD is an inventor (WO2017EP84625), VB, YM, MH, MH, SS, CB, SH, AB No competing interests declared, Rd The Goethe University Frankfurt has filed a patent related to this work on which RDB is an inventor (WO2017EP84625), SW The Goethe University Frankfurt has filed a patent related to this work on which SW is an inventor (WO2017EP84625), ID is co-founder, shareholder and CEO of Vivlion GmbH in Gründung. Also a senior editor of eLife, AE The Goethe University Frankfurt has filed a patent related to this work on which AE is an inventor (WO2017EP84625), MK The Goethe University Frankfurt has filed a patent related to this work on which MK is an inventor (WO2017EP84625). Also co-founder, shareholder and CSO of Vivlion GmbH in Gründung, (© 2019, Wegner et al.)