ACPYPE - AnteChamber PYthon Parser interfacE

Many molecular docking programs are available nowadays, and thus it is of great practical value to evaluate and compare their performance. We have conducted an extensive evaluation of four popular commercial molecular docking programs, including Glide, GOLD, LigandFit, and Surflex. Our test set consists of 195 protein-ligand complexes with high-resolution crystal structures (resolution <or=2.5 A) and reliable binding data [dissociation constant (K(d)) or inhibition constant (K(i))], which are selected from the PDBbind database with an emphasis on diversity. The top-ranked solutions produced by these programs are compared to the native ligand binding poses observed in crystal structures. Glide and GOLD demonstrate better accuracy than the other two on the entire test set. Their results are also less sensitive to the starting structures for docking. Comparison of the results produced by these programs at three different computation levels reveal that their accuracy are not always proportional to CPU cost as one may expect. The binding scores of the top-ranked solutions produced by these programs are in low to moderate correlations with experimentally measured binding data. Further analyses on the outcomes of these programs on three suites of subsets of protein-ligand complexes indicate that these programs are less capable to handle really flexible ligands and relatively flat binding sites, and they have different preferences to hydrophilic/hydrophobic binding sites. Our evaluation can help other researchers to make reasonable choices among available molecular docking programs. It is also valuable for program developers to improve their methods further.

One big issue facing the industry today is an automated machine lack of flexibility for customization because it is designed by the manufacturers based on certain standards. In this research, it is developed customized application software for CNC (Computer Numerically Controlled) machines using open source platform. The application is enable us to create designs by means of fractal patterns using L-System, developed by turtle geometry interpretation and Python programming languages. The result of the application is the G-Code of fractal pattern formed by the method of L-System. In the experiment on the CNC machine, the G-Code of fractal pattern which involving the branching structure has been able to run well.

Purpose of research . The research is aimed at exploring the possibilities of using cognitive technologies (CT) to effectively solve poorly formalized tasks in the field of analyzing large amounts of textual data. Special attention is paid to the task of automatic text annotation, which is one of the most important problems of modern science and technology, stimulating the active development of artificial intelligence and machine learning methods. Methods . To achieve this goal, an extractive method of automatic summary compilation was used. This approach involves selecting the most significant fragments of the source text by highlighting individual sentences or phrases based on certain criteria. The following selection criteria were used: frequency of occurrence of words; semantic importance of words and expressions; position of sentences within the document. These indicators allow you to highlight the main thoughts of the text and create a compact summary that preserves the meaning of the source material. Results . During the experiments, a program was developed in the Python programming language that implements the extracted method of taking notes. The algorithm is based on an analysis of the frequency of occurrence of keywords in the text, which ensures an effective assessment of the significance of each sentence. The program has a number of advantages: simplicity of implementation and operation; open source code, which makes it easy to adapt the solution to the specific needs of users; high efficiency in processing significant amounts of textual information. The developed tool demonstrates the ability to effectively create a summary of the text, while maintaining the basic meaning and structure of the original content. Conclusion . The use of a cognitive algorithm has significantly improved the productivity of the text information analysis and processing process. The proposed methodology is capable of automating routine operations for making notes, helping specialists quickly get a general idea of the contents of large documents and publications. This is especially important in the context of the modern information society, characterized by ever-growing data flows that require rapid and high-quality comprehension. Thus, the study showed the prospects of introducing cognitive technologies into the field of automation of intellectual work and offered a practical solution to the urgent problem of taking notes on large amounts of information, which can become an important assistant for specialists and researchers.

Cancer is a heterogeneous disease, showing intra- and inter-tumor genetic and phenotypic variability [1]. This variability translates to differential radiosensitivity and in consequence differential response to radiotherapy. Head and neck squamous cell carcinoma (HNSCC) accounts for around 5-10% of new cancer cases in developed countries [2]. It shows a heterogeneous response to radiochemotherapy with loco-regional control and 5 years overall survival ranging from below 50% to 80%. Few molecular factors were linked to outcome prognosis in HNSCC, for example human papilloma virus (HPV) infection. However, tissue-based biomarkers from tumor biopsies may not account for intra-tumor heterogeneity [3, 4]. This PhD project aims to identify new tumor phenotypes in HNSCC related to worse prognosis of treatment outcome using medical imaging techniques, which provides a 3D surrogate of tumor biology. Tumor density, metabolism and perfusion were studied in respect to different HNSCC subtypes and radiochemotherapy outcome. A quantitative and comprehensive image analysis method, radiomics, was used to link intra-tumor heterogeneity and treatment outcome. Radiomics comprises four types of descriptors: shape, intensity, texture and filter-based. It not only quantifies general properties of a tumor, for example higher metabolic activity, but also provides information about the intra-tumor heterogeneity. In the first subproject, I have analyzed tumor perfusion, metabolism and their correlation in subgroups of HNSCC based on: tumor subtype (oropharynx, hypopharynx, larynx and oral cavity), tumor stage (T1/T2 vs T3/T4) and HPV status. Computed tomography perfusion (CTP) and 18F-fludeoxyglucose positron emission tomography (18F-FDG PET), from 41 HNSCC patients were analyzed. Three perfusion parameters: blood volume (BV), blood flow (BF) and mean transit time (MTT), were computed. Difference in perfusion parameters between the gross tumor volume (GTV) and its surrounding tissue were investigated. Tumor subgroups related to worse prognosis (T3/T4 and HPV negative) showed increased BV and MTT in comparison to surrounding healthy tissue. Additionally, I have shown that the correlation of FDG uptake and perfusion is tumor subgroup dependent. I have observed positive correlation only for HPV positive (r = 0.86, p = 0.04) and oropharyngeal (r=0.63, p = 0.05) cancer. CTP consists of repeated CT scans and is thus dose intensive. I have performed a separate study using Alderson phantom to adapt our clinical CTP head and neck protocol. The endpoint was a decrease in delivered dose and maintenance of image quality. Our standard protocol on GE revolution CT is 100 kV, 80 mAs, 5 mm slice thickness and filtered back projection algorithm. I have adapted the percentage of an adaptive statistical iterative reconstruction (ASiR), slice thickness, tube current and voltage. The signal to noise ratio was measured in 7 predefined regions of interest and the effective dose was estimated using thermoluminescent dosimeters. The optimized protocol used 80 kV, a tube current adapted based on anatomy from 15 to 80 mAs, 2.5 mm slice thickness and 50% of ASiR reconstruction. The effective dose was decreased by factor of 2 whereas the image quality was maintained. In the second part of the thesis, I have investigated radiomics for its ability to predict treatment outcome and its correlation to tumor biology. An in-house radiomics software implementation was developed in Python programing language (v 2.7). Most of the radiomics studies are performed using in-house implementations or open source codes and the implemented workflows are currently not fully standardized. Therefore, I have validated my implementation against implementation from MAASTRO clinic, Maastricht, the Netherlands. I have also used both implementations to train local tumor control models based on 18F-FDG PET imaging 3 months post-radiochemotherapy (128 patients). Only 80 out of 649 radiomic features, available in both implementations and based on the same mathematical definition, were reproducible between the implementations (intraclass correlation coefficient ICC &gt; 0.8). In the univariate Cox regression feature’s prognostic power depended strongly on used implementation. The main causes of irreproducibility were differences in contour mask creation, translation of bin size to filtered images, and type of the used transform decimated vs undecimated wavelet transform. In another radiomics robustness study I have investigated the stability of radiomic features in respect to different CTP calculation factors. Some of the CTP calculation factors are difficult to standardize (arterial input function definition and noise threshold in the calculation) and thus should be considered before linking CTP radiomics with clinical outcome. I have analyzed CTP scans in lung (n = 11) and head and neck cancer (n = 11). 255 out of 945 CTP radiomic features were stable in both tumor sites in respect to artery contouring and noise threshold. Among them, I have identified 10 groups of radiomic features, after the correction for inter-features correlations and correlation to tumor volume. These features should be further tested for their prognostic power. In the prognostic modeling, I have investigated the link between local tumor control and radiomics in HNSCC based on contrast-enhanced CT and 18F-FDG PET pre-treatment imaging. I have used two cohorts of patients: retrospective for models training (n &gt; 90 patients) and prospective cohort from institutional phase II study with a standardized imaging protocol for models validation (n &gt; 50 patients). I have observed that tumors more heterogeneous in CT density were at higher risk for tumor recurrence. This model had a higher prognostic power than model incorporating clinical prognostic factors (tumor stage, volume and HPV status) or combination of CT radiomics and clinical factors, concordance index (CI) in the validation cohort CIradiomics = 0.78, CIclinical = 0.73 and CIcombination = 0.76. In a follow-up study, I have investigated whether the inclusion of metabolic information can further improve radiomics-based local tumor control modeling. I have observed that round tumors (based on 18-FDG PET autosegmentation) with a focused region of high FDG uptake surrounded by a rim of low FDG uptake were linked with better prognosis. However, this model did not outperform the CT based model. In the validation cohort evaluated in this study, both models achieved CI around 0.7. Also the combination of PET and CT radiomics did not improve the predictions. Nevertheless, the PET radiomics model showed a better calibration, which may be linked to the presence of metal artifacts in CT in head and neck region. To link the abstract radiomic features with tumor biology, I have correlated CT radiomics with HPV status. I have observed that tumors more homogenous in CT density tend to be HPV positive. Although, this signature (set of radiomic features) has a similar interpretation to local tumor control signature, it comprised different features and the signatures were not correlated with each other. For example local tumor control CT radiomics model was also prognostic in HPV negative subgroup of patients. In summary, I have shown that biological information can be recovered even from simple morphological imaging (CT). Additionally, I have identified imaging signatures, based on differences in perfusion between tumor and its surrounding as well as CT and PET radiomics, which were linked with worse outcome prognosis. These signatures need to be further validated in an external cohort of patients and treatment intensification options for worse prognosis groups have to be defined.

The context of this work is specification, detection and ultimately removal of detectable harmful patterns in source code that are associated with defects in design and implementation of software. In particular, we investigate five code smells and four antipatterns previously defined in papers and books. Our inquiry is about detecting those in source code written in Python programming language, which is substantially different from all prior research, most of which concerns Java or C-like languages. Our approach was that of software engineers: we have processed existing research literature on the topic, extracted both the abstract definitions of nine design defects and their concrete implementation specifications, implemented them all in a tool we have programmed and let it loose on a huge test set obtained from open source code from thousands of GitHub projects. When it comes to knowledge, we have found that more than twice as many methods in Python can be considered too long (statistically extremely longer than their neighbours within the same project) than in Java, but long parameter lists are seven times less likely to be found in Python code than in Java code. We have also found that Functional Decomposition, the way it was defined for Java, is not found in the Python code at all, and Spaghetti Code and God Classes are extremely rare there as well. The grounding and the confidence in these results comes from the fact that we have performed our experiments on 32'058'823 lines of Python code, which is by far the largest test set for a freely available Python parser. We have also designed the experiment in such a way that it aligned with prior research on design defect detection in Java in order to ease the comparison if we treat our own actions as a replication. Thus, the importance of the work is both in the unique open Python grammar of highest quality, tested on millions of lines of code, and in the design defect detection tool which works on something else than Java.

Developing models that simulate the behavior of different types of interacting biological cells can be a very time consuming and error prone task. CompuCell3D is an open source application that addresses this challenge. It provides interactive and customizable visualizations that help a user detect when a model is producing the desired behavior and when it is failing. It also allows for high quality image generation for publications and presentations. CompuCell3D uses the Python programming language which allows for easy extensions. Examples are provided for performing graph analyses of cell connectivity.

This paper introduces Pyff, the Pythonic Feedback Framework for feedback applications and stimulus presentation. Pyff provides a platform independent framework that allows users to develop and run neuroscientific experiments in the programming language Python. Existing solutions have mostly been implemented in C++, which makes for a rather tedious programming task for non-computer-scientists, or in Matlab, which is not well suited for more advanced visual or auditory applications. Pyff was designed to make experimental paradigms (i.e. feedback and stimulus applications) easily programmable. It includes base classes for various types of common feedbacks and stimuli as well as useful libraries for external hardware such as eyetrackers. Pyff is also equipped with a steadily growing set of ready-to-use feedbacks and stimuli. It can be used as a standalone application, for instance providing stimulus presentation in psychophysics experiments, or within a closed loop such as in biofeedback or brain-computer interfacing experiments. Pyff communicates with other systems via a standardized communication protocol and is therefore suitable to be used with any system that may be adapted to send its data in the specified format. Having such a general, open source framework will help foster a fruitful exchange of experimental paradigms between research groups. In particular, it will decrease the need of reprogramming standard paradigms, ease the reproducibility of published results, and naturally entail some standardization of stimulus presentation.

With the growth of protein structure data, the analysis of molecular interactions between ligands and their target molecules is gaining importance. PLIP, the protein–ligand interaction profiler, detects and visualises these interactions and provides data in formats suitable for further processing. PLIP has proven very successful in applications ranging from the characterisation of docking experiments to the assessment of novel ligand–protein complexes. Besides ligand–protein interactions, interactions with DNA and RNA play a vital role in many applications, such as drugs targeting DNA or RNA-binding proteins. To date, over 7% of all 3D structures in the Protein Data Bank include DNA or RNA. Therefore, we extended PLIP to encompass these important molecules. We demonstrate the power of this extension with examples of a cancer drug binding to a DNA target, and an RNA–protein complex central to a neurological disease. PLIP is available online at https://plip-tool.biotec.tu-dresden.de and as open source code. So far, the engine has served over a million queries and the source code has been downloaded several thousand times.

Current minimization programs do not permit full control over different aspects of minimization algorithm such as distance or probability measures and may not allow for unequal allocation ratios. This article describes the implementation of “MinimPy” an open-source minimization program in Python programming language, which provides full customizetion of minimization features. MinimPy supports naive and biased coin minimization together with various new and classic distance measures. Data syncing is provided to facilitate minimization of multicenter trial over the network. MinimPy can easily be modified to fit special needs of clinical trials and in particular change it to a pure web application, though it currently supports network syncing of data in multi-center trials using network repositories.

Transmission electron microscopy (TEM) is one of the most powerful tools for characterizing a wide variety of materials. Rapid developments in instrumentation are allowing additional information to be gleaned from advanced imaging techniques and bringing many new people into the field. At the same time, open-source code has become an indispensable tool for research and teaching, and as in many other fields, the Python programming language has become the standard choice for TEM practitioners. Although traditional textbooks continue to play an important role in training the next geneeration of TEM researchers, these concurrent developments in computing are enabling new kinds of educational resources. In this work, we provide a practical and self-contained guide to scanning and transmission electron microscopy image simulations, giving learners a theoretical basis from which to develop an intuitive appreciation of how different imaging modalities work and how the choice of parameters affect the resulting images. Our interactive examples are based on fully open-source software packages, most notably the abTEM code that is becoming the standard in the field, with all the code provided alongside the article. Our aim is to help both newcomers and more experienced microscopists who may not yet be familiar with simulations to build understanding towards making image simulations a routine part of their learning and research.

In recent years, open-source applications have replaced proprietary software in many fields. Especially open-source software tools based on Linux operating system have wide range of utilization. In terms of CNC solutions, an open-source system LinuxCNC can be used. However, the LinuxCNC control software and the graphical user interface (GUI) could be developed only on top of Hardware Abstraction Layer. Nevertheless, the LinuxCNC community provided Python Interface, which allows for controlling CNC machine using Python programming language, therefore whole control software can be developed in Python. The paper focuses on a development of a multi-process control software mainly for in-house developed loading devices operated at our institute. The software tool is based on the LinuxCNC Python Interface and Qt framework, which gives the software an ability to be modular and effectively adapted for various devices.

Virtual Laboratory on Nonlinear Control

This study aims at investigating procedures of semantic and linguistic extraction of keywords from metadata of documents indexed in the Institutional Repository Unesp. For that purpose, a web crawler was developed, that collected 325.181 keywords from authors, in all fields of knowledge, from February 28th, 2013 to November 10th, 2021. The preparation of the collection, extraction and analysis environment used the Python programming language, composed of three program libraries: library requests, which allows manipulation of hyperlinks of webpages visited through web crawler; BeautifulSoup library, used to extract HTML data through webpage analysis; and Pandas library, which has an open code (free software) and stands for providing tools for high performance data manipulation and analysis. The final listing consisted of 273,485 keywords, which represents 15.9% of the listing initially collected. Results indicated that the most recurring problem was the duplication of keywords, with 51,696 duplicated keywords, representing indicators of inconsistencies in the search for documents. It is concluded that the refinement of keywords assigned by authors eliminates the incorporation of a set of symbols that do not represent the authors’ keywords with the same spelling, but with upper/lower case variations or lexical variations indexing different documents.

Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited docking power (or ability to successfully identify the correct pose) has been a major impediment to cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with physicochemical and geometrical features characterizing protein-ligand complexes to predict the native or near-native pose of a ligand docked to a receptor protein’s binding site. We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark datasets on both diverse and homogeneous (protein-family-specific) test sets. We find that the best performing ML SF has a success rate of 80 % in identifying poses that are within 1 A root-mean-square deviation from the native poses of 65 different protein families. This is in comparison to a success rate of only 70 % achieved by the best conventional SF, ASP, employed in the commercial docking software GOLD. We also observed steady gains in the performance of the proposed ML SFs as the training set size was increased by considering more protein-ligand complexes and/or more computationally-generated poses for each complex.

J estimation based on regression machine learning applied to circumferential surface clad pipes with V groove weld