Selection Stage Research Articles

Developing a Hydrocarbon Electronic Book to Develop Students Critical Thinking Skills Through the 4S TMD Method

This study aimed to develop an electronic book on hydrocarbon study materials using the Four-Steps Teaching Material Development (4S TMD) method to develop students’ critical thinking skills. To achieve this goal, an electronic book was developed using the 4S TMD method, which will be tested using the Development Research (DR) research method which consists of three stages: design, development, and evaluation. At this stage the study was the first part of the development of teaching materials which consisted of four stages: selection, arrangement, characterization, and didactic reduction. The results of the research were focused on the selection stages. At the selection stage, the development of teaching materials begins with the development of learning indicators, the development of the concept of hydrocarbons based on curriculum demands, the development of basic concepts sourced from international textbooks, and finally, the development of skills that can be integrated with the concept of hydrocarbons. The results of the selection stage were evaluated by expert lecturers in the field of chemistry education.
 Keywords: electronic book, hydrocarbon, 4S TMD, critical thinking skills

Enhancing Fundraising with Digital Transaction in Indonesia: A Systematic Literature Review (SLR)

This research uses the Systematic Literature Review (SLR) method to investigate the optimization of collecting Zakat, Infaq and Sadaqah funds through digital transactions. The data analysis process uses the PRISMA diagram and includes the stages of identification, search, selection, data collection and analysis. From three main sources, researchers collected 20 references, consisting of theses and journals, which were analyzed to identify findings related to collecting ZIS funds with digital transactions in Indonesia. The results reflect the digitalization trend, where zakat institutions are increasingly using information technology to increase efficiency, accessibility and transparency in the process of collecting ZIS funds. This trend involves utilizing mobile applications, QRIS, and digital platforms, enabling faster and more efficient fund collection. Other benefits include ease of access, increased transparency, and more effective involvement of muzakki. Using digital technology also enables data analysis, better fund management and the development of sustainable business models. The COVID-19 pandemic has triggered new strategies in collecting ZIS funds, with zakat institutions turning to social media and digital platforms as alternatives to raise funds. Online payment services with mobile QR Codes are key in maintaining the continuity of ZIS fund collection amidst physical restrictions. Socialization efforts, easy access, and partnerships with various parties are the main strategies for zakat institutions to remain relevant and effective in facing the challenges of the pandemic. This research focuses on the central role of digitalization in increasing the efficiency and effectiveness of collecting ZIS funds, such as paying attention to social changes, namely the COVID-19 pandemic. By using digital technology, zakat institutions can continue to innovate and meet the needs of muzakki, so that they continue to play their role in helping people in need.

Bacterial Leaf Spot Susceptibility Screening of Chili Pepper Cultivars Using qPCR Determination of Xanthomonas euvesicatoria pv. euvesicatoria Titers.

Bacterial leaf spot is a serious disease of chili pepper (Capsicum spp.) caused by Xanthomonas euvesicatoria pv. euvesicatoria. Conventional resistance screening is time and resource intensive. It was considered that a quick and simple determination of cultivar susceptibility could be achieved through estimating bacterial titers of inoculated plants. A SYBR quantitative polymerase chain reaction (qPCR)-based assay was compared with conventional PCR, then used to detect and enumerate pathogen titers in serial dilutions and DNA extracted from infected plant leaves. The qPCR detection limit was approximately 1 CFU µl-1, 10 times more sensitive than conventional PCR. A linear correlation (R2 = 0.994) was obtained from the standard curve comparing plate-truthed serial dilutions of the pathogen with the qPCR cycle threshold. Six strains were used to inoculate cultivars Hugo and Warlock. One strain, X. euvesicatoria pv. euvesicatoria BRIP62403, was consistently the most virulent based on visual symptoms and pathogen titers in planta inferred by qPCR performed on DNA extracted from infected leaves 2 and 6 weeks postinoculation. Visual observations 6 weeks after inoculation were highly correlated (R2= 0.8254) to pathogen titers. The qPCR method was used to categorize 20 chili pepper cultivars 2 weeks after inoculation. A high positive correlation (R2 = 0.6826) was observed between visual scoring and pathogen titers from 20 chili pepper cultivars, facilitating categorization of susceptible, intermediate, and resistant cultivars. The qPCR approach developed here facilitates susceptibility screening of chili pepper cultivars at an early stage of selection and could be readily adapted to a range of other pathosystems.

BDR6D: Bidirectional Deep Residual Fusion Network for 6D Pose Estimation

Six-dimensional (6D) pose estimation is an important branch in the field of robotics focused on enhancing the ability of robots to manipulate and grasp objects. The latest research trend in 6D pose estimation is to directly predict the positions of two-dimensional (2D) keypoints from a single red, green, and blue (RGB) image through convolutional neural networks (CNNs) and establish a corresponding relationship with the three-dimensional (3D) keypoints of the model. Then, the perspective-n-point (PnP) algorithm is used to recover the 6D pose parameters. Currently, two challenges are encountered in pose estimation based on an RGB image. On the one hand, an RGB image lacks depth information, and it is thus difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. In this paper, we propose a bidirectional depth residual fusion network with a depth prediction (DP) network to estimate the 6D poses of objects (BDR6D). The BDR6D network predicts the depth information of objects using an RGB image, converts the depth information into point cloud information, and performs feature extraction and representation together with the RGB information during the feature extraction and representation stages. Specifically, the RGB image is fed into the BDR6D network, the DP network predicts the depth information of the objects in the image, and the depth map and RGB image are input into a point cloud network (PCN) and CNN, respectively, for feature extraction and representation. We build the bidirectional depth residual (BDR) structure so that the CNN and PCN can share information during feature extraction and representation. This approach allows the two networks to use each other’s local and global information to improve feature extraction and representation. For the keypoint selection stage, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of the object of interest. We evaluate the proposed method with three benchmark datasets and compare it with other 6D pose estimation algorithms. The experimental results show that our method outperforms the state-of-the-art approach. Finally, we deploy our proposed method in conjunction with the Universal Robots 5 manipulator (UR5) robot to grasp and manipulate objects. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The purpose of this paper is to solve the problem of 6D pose estimation for robot grasping. The existing RGB image-based pose estimation approach faces two challenges. On the one hand, a single RGB image lacks depth information, so that it is difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. To solve the above problems, a novel network that can predict the depth information of objects from an RGB image and fuse the depth information with the RGB information to estimate the 6D pose of objects is proposed. Furthermore, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of objects of interest. We evaluate the proposed approach based on three benchmark datasets and the UR5 robot platform and verify that our method is effective.

Programming Language Selection for The Development of Deep Learning Library

Recently, deep learning has become very successful in various applications, leading to an increasing need for software tools to keep up with the rapid pace of innovation in deep learning research. As a result, we suggested the development of a software library related to deep learning that would be useful for researchers and practitioners in academia and industry for their research endeavors. The programming language is the core of deep learning library development, so this paper describes the selection stage to find the most suitable programming language for developing a deep learning library based on two criteria, including coverage on many projects and the ability to handle high-dimensional array processing. We addressed the comparison of programming languages with two approaches. First, we looked for the most demanding programming languages for AI Jobs by conducting a data-driven approach against the data gathered from several Job-Hunting Platforms. Then, we found the findings that imply Python, C++, and Java as the top three. After that, we compared the three most widely used programming languages by calculating interval time to three different programs that contain an array of exploitation processes. Based on the result of the experiments that were executed in the computer terminal, Java outperformed Python and C++ in two of the three experiments conducted with 5,4047 milliseconds faster than C++ and 231,1639 milliseconds faster than Python to run quick sort algorithm for arrays that contain 100.000 integer values.

Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS

An intrusion detection system (IDS) is a security technology designed to identify and monitor suspicious activity in a computer network or system and detect potential attacks or security breaches. The importance of accuracy in IDS must be addressed, given that the response to any alert or activity generated by the system must be precise and measurable. However, achieving high accuracy in IDS requires a process that takes work. The complex network environment and the diversity of attacks led to significant challenges in developing IDS. The application of algorithms and optimization techniques needs to be considered to improve the accuracy of IDS. Support vector machine (SVM) is one data mining method with a high accuracy level in classifying network data packet patterns. A feature selection stage is needed for an optimal classification process, which can also be applied to SVM. Feature selection is an essential step in the data preprocessing phase; optimization of data input can improve the performance of the SVM algorithm, so this study compares the performance between feature selection algorithms, namely Information Gain Ratio and Chi-Square, and then classifies IDS data using the SVM algorithm. This outcome implies the importance of selecting the right features to develop an effective IDS.

Dashboard Design for New Employee Candidate Program at XYZ Bank Using Tabelau

Bank XYZ is one of the government-owned companies engaged in banking and is one of the largest banks in Indonesia. Bank XYZ has a division called XYZ Corpu which aims to support the company's business operations by developing employee competencies. One of the training programs conducted at XYZ Corpu is the ODP (Officer Development Program). ODP participants are prospective XYZ Bank employees who enter through several selection stages and then carry out training for 12 months which applies a knockout system if the participants are not competent. ODP participants will undergo 12 months of education and training with several stages of the journey. To make it easier to get important information such as the distribution of participants based on the location of the placement division, placement city, distribution of journey end dates, and journey stages. So this research designs a dashboard to make it easier for management to get important information for decision making. This research uses Pentaho tools to perform ETL processes and Tableau tools to create dashboards. Based on the design made, the dashboard user/manager can anticipate or detect early delays in the process of making rundowns for the next journey stage through this dashboard. Based on the OLAP design of this research, the ODP distribution fact table and dimension table consisting of location, journey, city, start date, end date, batch, gender, university and major are formed. Based on the design made, dashboard users/managers can anticipate or detect early delays in the process of making rundowns for the next journey stage through this dashboard. With this dashboard, it can find out the most universities and study programs for more precise decision making. The dashboard created in this study has a data filtering feature that is useful for searching for certain data more quickly.

Visual cue spatial context affects performance of anticipatory postural adjustments

Past research indicates that anticipatory postural adjustment (APA) errors may be due to the incorrect selection of responses to visual stimuli. In the current study we used the Simon task as a methodological tool to challenge the response selection stage of processing by presenting visual cues with conflicting spatial context; in this case generating a step response to a left pointing arrow which appears to the participant's right side or vice versa. We expected greater mediolateral APA errors, delayed APA and step onset times, and greater lateral CoP displacement prior to stepping for visual cues with incongruent spatial contexts compared to cues with congruent. Thirteen healthy young adults completed step initiation trials (n = 40) from a force platform while whole-body kinematic motion was tracked. Participants were presented with arrows pointing to the left or right, indicating to step with the left or right limb, respectively. These arrows were presented on the same side as the desired step direction (congruent) or the opposite side (incongruent). Results revealed that incongruent trials resulted in significantly more incidences of mediolateral APA errors and greater mediolateral CoP deviations during the APA compared to congruent visual cue context trials. No effects were observed for the temporal outcomes, suggesting that young adults can maintain temporal execution of steps despite these motor control errors. This study demonstrates that the spatial context of visual information significantly impacts the success of response selection processes during step initiation, furthering our knowledge of how humans integrate visual information to initiate whole body movement.

Features of selection of candidates for units of special operations forces of foreign countries by the criterion of physical preparedness

Features of selection of Military Special Operations Forces personnel of foreign countries by the criterion of physical preparedness are considered. Scientific, methodological literature on the research topic was studied, specialists were interviewed. Selection of candidates for special operations forces is consists of several stages. It has been established that the physical fitness of candidates is checked at all stages of selection. Determining the aerobic and strength endurance of candidates is a mandatory element of testing their physical preparedness. Tests are selected based on the requirements of the future professional activity of the fighters. Also take into account how these tests correlate to injury risk or professional tasks performance. The main tests for determining strength endurance choose exercises with local load distribution. Long-distance running is the main exercise for determining the level of aerobic endurance of military personnel. Swimming exercises is included in the selection programmes for most special operations units. The speed and coordination abilities of servicemen are tested with the help of various variants of the shuttle run. There is no gender or age differentiation when assessing candidates. The selection of candidates is made from several phases and involves complication of standards. The selection program also includes applied physical exercises that are performed in uniform, namely: swimming, marching, overcoming an obstacle course, etc. The system of selection is based on the motivation of servicemen. They tend to score more points than their competitors.

Features of the influence of special strength training on performance in competitive activities in Mixed Martial Arts (example of horting)

The article is devoted to the study of the problem of the specific influence of strength training in horting on the effectiveness of the implementation of the functional adaptation potential of the athletes' body and their level of technical and tactical skill in the process of competitive activity. In the process of the research, a comparative analysis of the results of the matches between the athletes of all three experimental groups identified at the stage of selection for the championship of Ukraine was carried out after three months of using experimental models of strength training classes and during the competition for the selection for the Cup of Ukraine. The results of the study showed that the number of victories of athletes of the second group at this stage of the study during qualifying matches decreased by 41.7% compared to the previous results. At that time, on the contrary, among the athletes of the third group, we observe an increase in the effectiveness of competitive activity by 62.5% compared to the number of victories recorded during qualifying matches for the championship of Ukraine before the use of the experimental model of strength training classes. In the process of analyzing the results of competitive activity recorded in at the end of the study, during qualifying matches for the Cup of Ukraine, it was found that among the athletes of the second group, compared to the magnificent indicators of the previous control, we observed the largest decrease in the number of victories by 4 times due to technical knockout. At the same time, among the athletes of this group, the indicator of the number of victories obtained due to knockout decreased by 50.0% compared to the results that were revealed during the qualifying competitions before the start of the application of the proposed experimental model of training sessions. The corresponding negative dynamics to a 3-fold decrease in the number of victories obtained in matches with athletes of the third group in the conditions of the use of suffocating techniques, we record in the participants of the second group at this stage of the study compared to the results found in previous competitions.

Abstract 605: ABBV-101, a highly potent and selective clinical stage Bruton tyrosine kinase degrader, overcomes BTK mutation-induced resistance to BTK inhibitors

Abstract Bruton tyrosine kinase (BTK), a clinically validated target for various B-cell malignancies, plays a critical role in regulating cell growth, adhesion, and homing to lymphoid tissues that provide a microenvironment favorable to cancer cells. Despite the success of approved covalent BTK inhibitors in diseases such as CLL, most patients do not achieve complete response with monotherapy and eventually relapse, often via the emergence of the BTK-C481S mutation. Although second-generation reversible BTK inhibitors may temporarily overcome the C481S mutation, novel BTK mutations associated with resistance emerged in the clinic recently. Additionally, covalent BTK inhibitors have not shown sufficient differentiation from standard-of-care (SoC) in B-cell malignancies, such as diffuse large B cell lymphoma (DLBCL) to warrant approval to date. To address these unmet needs, a next-generation orally bioavailable BTK degrader ABBV-101 has been developed. Unlike BTK inhibitors that solely bind to and impede the catalytic domain of BTK, ABBV-101 eliminates the protein in a highly selective fashion. This degradation mechanism targets both the catalytic and scaffolding functions of BTK, and thus may enable deeper and more durable responses in patients with B-cell malignancies. Cellular activity of ABBV-101 was determined in a panel of DLBCL cell lines. We show that most non-germinal center B cell DLBCL (non-GCB-DLBCL) cell lines are sensitive to ABBV-101. To determine whether ABBV-101 could overcome BTK mutations-induced resistance, we engineered human DLBCL cell line TMD8 using CRISPR technology to express these BTK mutations and tested for sensitivity to ABBV-101 and BTK inhibitors. We show that ABBV-101 demonstrates similar potent activity against all BTK mutations associated with resistance to both covalent and reversible BTK inhibitors. The modulation of downstream pathway by ABBV-101 and how it differentiates from other BTK inhibitors will be presented. Further, ABBV-101 induces complete tumor regression in the BTK-C481S TMD8 DLBCL xenograft model. A spontaneous mouse CLL model carrying BTK-C481S mutation was developed through crossing BTK-C481S knock-in mice with the Emu-TCL1 transgenic mice in the C57BL/6 background. ABBV-101 completely inhibits BTK-C481S Emu-TCL1 CLL burden increase in the blood compartment and reduces CLL burden in the spleen and lymph nodes, whereas covalent BTK inhibitors show no activity. Finally, combination with a BCL-2 inhibitor enhances the efficacy of ABBV-101 in CLL and DLBCL models. Taken together, our results suggest that ABBV-101 has potent in vitro and in vivo efficacy. ABBV-101 is currently in phase 1 clinical trial for a variety of B-cell malignancies. ClinicalTrials.gov identifier: NCT05753501 Citation Format: Chin Pan, Jacob Riehm, Tarikere L. Gururaja, Haiyan Li, Henry Nguyen, Yongjiao Zhai, Xiaoping Xie, Rong-Xian Ding, Rebecca Matthew, Hana Y. Hoh, Zhaozhong J. Jia, Bo Liu, Cassandra P. Shu, Claudina A. Stevenson, Christine Will, Federico Innocenti, Wissam Assaily, Lloyd T. Lam, Alexey Rivkin. ABBV-101, a highly potent and selective clinical stage Bruton tyrosine kinase degrader, overcomes BTK mutation-induced resistance to BTK inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 605.

Local regularization assisted split augmented Lagrangian shrinkage algorithm for feature selection in condition monitoring

Feature selection plays a vital role in improving the efficiency and accuracy of condition monitoring by constructing sparse but effective models. In this study, an advanced feature selection algorithm named the local regularization assisted split augmented Lagrangian shrinkage algorithm (LR-SALSA) is proposed. The feature selection is realized by solving a l1-norm optimization problem, which usually selects more sparse and representative features at less computational costs. The proposed algorithm operates in two stages, namely variable selection and coefficient estimation. In the stage of variable selection, the primal problem is converted into three subproblems which can be solved separately. Then individual penalty parameters are applied to every coefficient of the model when dealing with the first subproblem. Under the Bayesian evidence framework, an iterative algorithm is derived to optimize these hyperparameters. During the optimization process, redundant variables will be pruned to guarantee model sparsity and improve computational efficiency at the same time. In the second stage, the coefficients for the selected model terms are determined using the least squares technique. The superior performance and efficiency of the proposed LR-SALSA method are validated through two numerical examples and a real-world cutting tool wear prediction case study. Compared with the existing methods, the proposed method can generate a sparse model and ensure a good trade-off between estimation accuracy and computational efficiency.

Mayfly Optimization for Real-World Problems: A Review of Applications and Challenges

The Mayfly Optimization Algorithm (MO), inspired by the swarming and mating behavior of adult mayflies, has emerged as a powerful tool for tackling optimization problems, particularly those seeking minimum values. This review paper delves into the core principles of MO, exploring its stages of swarming, velocity update, movement, mating, and selection. We analyze the strengths of MO, including its ability to balance exploration and exploitation during the search process, leading to well-converged solutions. Additionally, the paper examines recent advancements in MO that address potential limitations. We discuss how incorporating techniques like greedy selection and auto-termination can enhance the convergence speed and efficiency of the algorithm. Furthermore, the review explores various applications of MO across diverse fields, highlighting its potential for solving real-world minimization problems. Finally, we identify and discuss ongoing research directions, including hybridization with other algorithms and exploration of advanced termination strategies. This review paper aims to provide a comprehensive understanding of the Mayfly Optimization Algorithm, its capabilities, and promising areas for future development. Index Terms— Mayfly optimization, particle swarm optimization, swarm intelligence, nature inspired algorithm.

Sustainable water solutions:a Six Sigma approach to membrane-based filtration system design

Water contamination is a major problem nowadays which can not only be solved through technological innovations but also requires educational innovation. The contamination of water is caused by discharging harmful pollutants into the water. These harmful contaminants cause different diseases. The significance of water filtration has grown in recent years. The quality of water is affected majorly by residual waste, bacteria, and so on. Based upon these issues, the Six Sigma methodology is used in this research for the design of a portable filtration system. This methodology is based on five steps that align with the computational competencies involving abstraction, decomposition of problem, and algorithmic thinking. Initially, a questionnaire approach is used to identify the need for a portable water filter for potential users. The Quality Function Development (QFD) matrix is used to measure the user’s needs. Based on the user’s information, a decision matrix tool is being used in the Analyze stage. After this theoretical concept is generated, and selection is made among various options. The complete drawing was made in the design stage after several stages of concept generation and selection. Then a prototype is developed to conduct proof of concept testing. The hollow fiber membrane (HFM) that is being used is manufactured usually by melt spinning, dry spinning, and wet spinning. But usually, a wet spinning method is predominantly used for manufacturing hollow fiber membranes. Polymer liquid like polyvinyl chloride (PVC) is used for the manufacturing of membranes with other liquids in different ratios. The size of pores varies from 0.01 to 0.1 microns. The flux rate usually depends upon the volume, length, and size of the cartridge. Backwashing at regular intervals is done for the presentation of fooling due to the accumulation of solutes. This filtration system is also proficient in rejecting bacteria that are being found in water and soil. This is done by a coliform test that is being performed for bacteria. The porosity of the membrane is also affected by the concertation of polyvinyl chloride (PVC) as the concentration of polyethylene glycol increases the porosity of the membrane decreases. A Chemical Oxygen Demand test is also performed to check the presence of organic matter in water. After filtration, no organic matter was manifested in the water. Design for Six Sigma in a portable filtration system that uses membrane for filtration is a good start in looking for a new alternative concept. The implication of this research presents a multifaceted solution to water contamination issues, offering educational outreach programs, STEM education integration, community engagement, and innovative competitions as integral components for fostering awareness, sustainable practices, and creative solutions in the pursuit of clean water.

Исследование психодиагностических критериев оценки и прогнозирования профессиональной надежности офицеров войск национальной гвардии

The article is devoted to the professional reliability of officers of the National Guard troops in general, the criteria for its assessment and forecasting in particular. This area of research in the context of modern military threats is of particular importance for improving the effectiveness of the psychological support system both at the stage of professional psychological selection and further psychological support. The results of a retrospective analysis of psychodiagnostic indicators of professional psychological selection of two groups of subjects with different levels of professional reliability (n = 39 and n = 50) with a total sample of the examined (n = 221) are presented. As a result, 18 psychodiagnostic indicators were identified, which, in accordance with the design of the study, have the potential to further assess and predict the professional reliability of officers of the National Guard troops.

The Urgency Establishing a Sharia Land Bank To Accelerate Agrarian Reform

Researchers believe that the existence of a Sharia Land Bank in Indonesia which is based on the concepts of al-ilah and al-musawah can accelerate agrarian reform in Indonesia. To ensure this, the researcher presented this research using standard methodological articles in scientific research that are commonly used. This research is a qualitative research with a descriptive approach related to the Land Bank, Agrarian Reform, and the experience of the concepts of al-ilah and al-musawah in the Sharia Land Bank which was initiated by the researcher. The data used in this research is secondary data that researchers obtained from scientific articles, books, statutory regulations, news from credible websites, and other sources that are usually used in every research and are certainly related to Land Banks, Agrarian Reform, and values in Islamic law such as Al-Adalah and Al-Musawah. The data that the researcher managed to obtain was analyzed using the stages of data collection, data selection, data reduction, data presentation, and drawing conclusions. The result in this article show first, The Land Bank functions to collect land, manage land, manage land and provide land to overcome inequality in land ownership and accelerate agrarian reform. The existence of a Land Bank which has an unclear concept and researchers believe could make the Land Bank a weakness in its implementation. Second, The presence of the Sharia Land Bank concept which is a refinement of the Conventional Land Bank concept by upholding the values of al-adadlah (justice) and al-musawah (equality) can be a guide for Land Bank employees, Land Bank Officials and Government officials others in implementing the concepts offered by not distinguishing between one group and another, being honest, and so on which can accelerate agrarian reform.

The Impact of Sample Storage on Blood Methylation: Towards Assessing Myelin Gene Methylation as a Biomarker for Progressive Multiple Sclerosis.

One of the major challenges in multiple sclerosis (MS) is to accurately monitor and quantify disability over time. Thus, there is a pressing need to identify new biomarkers for disease progression. Peripheral blood DNA methylation has been demonstrated to be an easily accessible and quantifiable marker in many neurodegenerative diseases. In this study, we aimed to investigate whether methylation patterns that were previously determined in chronic inactive white matter lesions of patients with progressive MS are also reflected in the blood, and whether the latter can serve as a biomarker for disease progression in MS. While our initial analysis revealed differences in the blood methylation state of important myelin-related genes between patients with progressive MS and controls, these findings could not be validated in other independent patient cohorts. Subsequent investigation suggests that sample storage can selectively influence DNA methylation patterns, potentially hindering accurate epigenetic analysis. Therefore, sample storage time should be taken into consideration during the initial sample selection stage in biomarker studies.

Microglial over-pruning of synapses during development in autism-associated SCN2A-deficient mice and human cerebral organoids.

Autism spectrum disorder (ASD) is a major neurodevelopmental disorder affecting 1 in 36 children in the United States. While neurons have been the focus of understanding ASD, an altered neuro-immune response in the brain may be closely associated with ASD, and a neuro-immune interaction could play a role in the disease progression. As the resident immune cells of the brain, microglia regulate brain development and homeostasis via core functions including phagocytosis of synapses. While ASD has been traditionally considered a polygenic disorder, recent large-scale human genetic studies have identified SCN2A deficiency as a leading monogenic cause of ASD and intellectual disability. We generated a Scn2a-deficient mouse model, which displays major behavioral and neuronal phenotypes. However, the role of microglia in this disease model is unknown. Here, we reported that Scn2a-deficient mice have impaired learning and memory, accompanied by reduced synaptic transmission and lower spine density in neurons of the hippocampus. Microglia in Scn2a-deficient mice are partially activated, exerting excessive phagocytic pruning of post-synapses related to the complement C3 cascades during selective developmental stages. The ablation of microglia using PLX3397 partially restores synaptic transmission and spine density. To extend our findings from rodents to human cells, we established a microglia-incorporated human cerebral organoid model carrying an SCN2A protein-truncating mutation identified in children with ASD. We found that human microglia display increased elimination of post-synapse in cerebral organoids carrying the SCN2A mutation. Our study establishes a key role of microglia in multi-species autism-associated models of SCN2A deficiency from mouse to human cells.

Responsible Research Assessment requires structural more than procedural reforms

In their target articles, Schönbrodt et al. (2022) and Gärtner et al. (2022) propose new metrics and their practical implementation to improve responsible research assessment. Generally, I welcome the inclusion of open science and scientific rigor into evaluating job candidates. However, the proposed reform mainly focuses on the first stage of selecting candidates who then continue towards a second stage of in-depth evaluation of research quality. Yet, this second selection stage is underdeveloped but likely more critical concerning responsible research assessment and hiring decisions. I argue that an adequate assessment of research quality at this second stage requires the representation of specific knowledge in the subfield of a discipline that the candidate should be hired for by the hiring committee. This is rarely achieved given the current structural organization of departments, especially in German-speaking countries, and potentially explains the reliance on suboptimal indicators such as h-index and Journal Impact factor. Therefore, I argue that responsible research assessment requires structural reform to ensure that institutions have several researchers in permanent positions with specific knowledge in different subfields to provide an adequate and responsible assessment of research quality by hiring committees at all evaluation stages.

Enhanced Anomaly Detection Framework for 6G Software-Defined Networks: Integration of Machine Learning, Deep Neural Networks, and Dynamic Telemetry

The advent of 6G networks ushers in a new era of intelligent network management, necessitating robust security measures to safeguard against emerging threats. This paper presents a comprehensive framework for anomaly detection tailored specifically for 6G Software-Defined Networks (SDNs), leveraging innovative ML), (DL), and dynamic telemetry techniques. The proposed framework, termed Anomaly Detection System for 6G SDNs, integrates ensemble learning (EL) algorithms and deep neural networks (DNNs) to detect anomalies within network traffic. Beginning with the preprocessing and feature selection stages, the proposed system employs an amalgam EL method to enhance the efficacy of anomaly detection. Datasets including CICDDOS2019, NSL KDD, CIC_IDS2017, and NB2015 undergo dimensionality reduction and feature subset determination to optimize performance. Furthermore, dynamic telemetry is seamlessly integrated into the proposed, enabling real- time monitoring and adaptive response mechanisms within SDN environments. By harnessing the flexibility and programmability of SDNs, the framework ensures a proactive defense against evolving threats, bolstering the security posture of 6G networks. Experimental evaluations demonstrate the effectiveness of ADS6SDN across diverse datasets, achieving high accuracies while minimizing false alarm rates. In conclusion, integrating ML, DL, and dynamic telemetry within the proposed approach offers a potent solution for enhancing the security and responsiveness of 6G SDNs. By leveraging the inherent advantages of SDN architectures, the framework not only fortifies network defenses against emerging threats but also ensures adaptability to the budding scenario of next-generation telecommunications.