2FAST2Q: A general-purpose sequence search and counting program for FASTQ files

The increasingly widespread use of next generation sequencing protocols has brought the need for the development of user-friendly raw data processing tools. Here, we present 2FAST2Q, a versatile and intuitive standalone program capable of extracting and counting feature occurrences in FASTQ files. 2FAST2Q can be used in any experimental setup that requires feature extraction from raw reads, being able to quickly handle mismatch alignments, nucleotide wise Phred score filtering, custom read trimming, and sequence searching within a single program. Using published CRISPRi datasets in which Escherichia coli and Mycobacterium tuberculosis gene essentiality, as well as host-cell sensitivity towards SARS-CoV2 infectivity were tested, we demonstrate that 2FAST2Q efficiently recapitulates the output in read counts per provided feature as with traditional pipelines. Moreover, we show how different FASTQ read filtering parameters impact downstream analysis, and suggest a default usage protocol. 2FAST2Q has a familiar user interface and uses a custom sequence mismatch search algorithm, taking advantage of Pythons numba module JIT runtime speeds. It is thus easier to use and faster than currently available tools, efficiently processing large CRISPRi-Seq or random-barcode sequencing datasets on any up-to-date laptop. 2FAST2Q is available as an executable file for all current operating systems without installation and as a Python3 module on the PyPI repository (available at https://veeninglab.com/2fast2q). We expect that 2FAST2Q will not only be useful for people working in microbiology but also for other fields in which amplicon sequencing data is generated.

Adversarial EXEmples

Recent work has shown that adversarial Windows malware samples—referred to as adversarial EXE mples in this article—can bypass machine learning-based detection relying on static code analysis by perturbing relatively few input bytes. To preserve malicious functionality, previous attacks either add bytes to existing non-functional areas of the file, potentially limiting their effectiveness, or require running computationally demanding validation steps to discard malware variants that do not correctly execute in sandbox environments. In this work, we overcome these limitations by developing a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks based on practical, functionality-preserving manipulations to the Windows Portable Executable file format. These attacks, named Full DOS , Extend , and Shift , inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section. Our experimental results show that these attacks outperform existing ones in both white-box and black-box scenarios, achieving a better tradeoff in terms of evasion rate and size of the injected payload, while also enabling evasion of models that have been shown to be robust to previous attacks. To facilitate reproducibility of our findings, we open source our framework and all the corresponding attack implementations as part of the secml-malware Python library. We conclude this work by discussing the limitations of current machine learning-based malware detectors, along with potential mitigation strategies based on embedding domain knowledge coming from subject-matter experts directly into the learning process.

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine-learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProt, GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models were observed to be more effective than other prediction tools. For example, the prediction of sites phosphorylated by the Akt, CKT, and PKA families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the Shapley additive explanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned with the goal of providing comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/index.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

ATLaS: Assistant Software for Life Scientists to Use in Calculations of Buffer Solutions

Many solutions such as percentage, molar and buffer solutions are used in all experiments conducted in life science laboratories. Although the preparation of the solutions is not difficult, miscalculations that can be made during intensive laboratory work negatively affect the experimental results. In order for the experiments to work correctly, the solutions must be prepared completely correctly. In this project, a software, ATLaS (Assistant Toolkit for Laboratory Solutions), has been developed to eliminate solution errors arising from calculations. Python programming language was used in the development of ATLaS. Tkinter and Pandas libraries were used in the program. ATLaS contains five main modules (1) Percent Solutions, (2) Molar Solutions, (3) Acid-Base Solutions, (4) Buffer Solutions and (5) Unit Converter. Main modules have sub-functions within themselves. With PyInstaller, the software was converted into a stand-alone executable file. The source code of ATLaS is available at https://github.com/cugur1978/ATLaS.

Pengembangan media pembelajaran berbantuan komputer berbasis RME untuk siswa SMP kelas VII pada materi perbandingan

This research and development is motivated by student’s difficulties in learning proportion material especially in determining the direct and inverse proportion material. The students are difficult to distinguish problem about direct and inverse proportion material. Moreover, at this time there has been no development of computer assisted learning media based RME on direct and inverse proportion material. This development aims to produce a computer assisted learning media on direct and inverse proportion material which are valid, practical, and effective. The result of research and development is “GO!MATH” courseware in the form of executable file in CD that can be use independently by students both in classroom or at home. This research and development is done by adapting Thiagarajan development model which only up to the third stage, because the stage develope due only for limited testing stage. The limited testing in this research involved nine student of SMP Brawijaya Smart School Malang which has heterogeneous ability. The results show that “GO!MATH” are valid, practical, and effective.

Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System

As the planet watches in shock the evolution of the COVID-19 pandemic, new forms of sophisticated, versatile, and extremely difficult-to-detect malware expose society and especially the global economy. Machine learning techniques are posing an increasingly important role in the field of malware identification and analysis. However, due to the complexity of the problem, the training of intelligent systems proves to be insufficient in recognizing advanced cyberthreats. The biggest challenge in information systems security using machine learning methods is to understand the polymorphism and metamorphism mechanisms used by malware developers and how to effectively address them. This work presents an innovative Artificial Evolutionary Fuzzy LSTM Immune System which, by using a heuristic machine learning method that combines evolutionary intelligence, Long-Short-Term Memory (LSTM), and fuzzy knowledge, proves to be able to adequately protect modern information system from Portable Executable Malware. The main innovation in the technical implementation of the proposed approach is the fact that the machine learning system can only be trained from raw bytes of an executable file to determine if the file is malicious. The performance of the proposed system was tested on a sophisticated dataset of high complexity, which emerged after extensive research on PE malware that offered us a realistic representation of their operating states. The high accuracy of the developed model significantly supports the validity of the proposed method. The final evaluation was carried out with in-depth comparisons to corresponding machine learning algorithms and it has revealed the superiority of the proposed immune system.

Design of Optimized Trading Strategies with Web Assembly

Web Assembly is growing and also the most widely studied area which interests many developers when it comes to performance and speed to make web development fast as ever. When it comes to speed and performance algorithms can perform faster computations. Algorithmic trading executes trade at a faster speed. It can buy and sell stocks within a fraction of milliseconds. However, selecting the right tools and technologies is extremely important in algorithmic trading. There are trading strategies which we can use to optimize our trade and increase the return gained on buying and selling stocks. But, choosing an efficient programming language is substantially important. A programming language with a low latency can leverage the trade. Most commonly used languages for algorithmic trading are C/C++, Java, C#, Python. Speed and performance are an essential factor in algorithmic trading. The main purpose of introducing web Assembly in trading as discussed above is speed and performance. Web Assembly is a low-level binary instruction which can execute any program on the web and it can deliver native like performance on the internet. Using Web Assembly, we can compile any code written in languages like C/C++, C#, Java, and python to wasm (Web Assembly executable file) and run on the browser. Web Assembly was developed by W3C, Mozilla Corporation, and Google.

DEVELOPING MULTIMEDIA-BASED ENGLISH LISTENING MATERIAL FOR ISLAMIC SENIOR HIGH SCHOOL

This study aims at designing and developing multimedia-based English listening material for the first year students of an Islamic Senior High School. The study was a Research and Development (R&D) using ADDIE model. The respondents were 77 students and 4 English teachers of the first-year students of MAN 1 Kerinci. The data of this research were obtained through observation checklist, interviews, questionnaires, validation sheet, and checklist of the product practicality. The findings of product validation and try-out show that the product were interesting and easy to use with minor revisions. Further, it facilitates the students to have self-learning listening. The final product of the study is an executable file (* exe) attached on the CD. The result of the product validation, as well as the try-out, indicates that the material developed is categorized as "valid". The practicality of the material based on teachers and students’ responses indicates that the material developed is "practical" to (Nunan, 1999) use in the classroom.

Malware Threat Detection using Deep Neural Networks

Malware threat detection is one of the most challenging tasks in the field of Information Security and the shortage of qualified personnel makes it even harder for people to keep their information secure. Moreover, malware design has evolved continuously, making it even more difficult for people to protect themselves from malware attacks. Thus, it is the need of the hour to improve the existing malware threat detection systems with modern deep learning algorithms. This paper focuses on bringing together a comprehensive study of various deep learning solutions for the detection of malware from its PE file (Portable Executable File) byte streams.

Automation of Formation Tops Estimation Dramatically Reduces Well Planning Process Duration

Aiming to make the well planning process leaner and agile focusing on duration reduction without compromising quality of deliverables, automation opportunities have been identified within the multi-discipline iterations. The two key criteria considered for the selection of the automation project were: Minimum deployment effort and Maximum value added in efficiency. The initial project objective was to calculate formation tops for a well engineer without requiring the intervention of a geoscientist using commercial software. The methodology utilized is the following: 1. Inputs: Well trajectory and Surfaces. 2. Process: The algorithm finds intersections between surfaces and well trajectory. Surfaces and trajectory are represented as a set of XYZ points. To find the intersection, the software iterates through each point of the trajectory from the top, comparing the depth of the projection to the target surface. The projected depth to the surface is found by 2D interpolation of the surface. Once the trajectory point becomes deeper than the surface projection, the intersection is estimated using geometrical considerations of similar triangles. 3. Deliverables: Estimated formation tops for the given trajectory. 4. Results: Simple in-house developed software enhanced well planning workflow in an Offshore Green Field. The software converted to single executable file and can be run on any device without the open-source software installed. Very accurate results achieved with proposed algorithm with a negligible difference of 0.5 feet with the geoscience traditional software. Well planning duration reduced from average 1 week to 1 or 2 days. The workload for well engineers and the asset team has been dramatically reduced. Reduction of the number of commercial geoscience software licenses required. Way forward: A test with a slightly modified code was used to generate formation tops for more than 400 well in a Long-Term Field Development Plan project for a Brown Field during feasibility study. Upscale to all the Fields within the organization. Improve User Interface for better adoption. Include more formats for both, trajectories, and surfaces. Reduce computing time. This project represents the first initiative in the organization aiming to automate the well planning process. Overall, it represents the beginning of a journey where multiple opportunities for automation can be achieved using an open-source coding software that allows any engineer with little to no experience coding to being able to generate solutions to address daily challenges.