Initial Step Research Articles

Advancing scholarly excellence at midcareer: the influence of exceptional others on faculty professional growth

PurposeThere is a need to support midcareer faculty who have demonstrated scholarly success but require additional development. The purpose of this study was to explore the effects of an initiative for “star faculty” at midcareer, with an emphasis on the role of exceptional others in their professional growth.Design/methodology/approachThis is an exploratory, single-site case study of a midcareer faculty excellence initiative. Data sources include document/site analysis and individual interviews.FindingsFindings reveal the value of “exceptional others” in professional growth among high-performing midcareer faculty. Perceptions about excellence at midcareer emerged as an antecedent to developing midcareer faculty members. Analysis generated themes in behaviors related to supporting midcareer scholars’ professional growth.Research limitations/implicationsThis study is an initial step toward refining concepts such as exceptional others, academic stars and scholarly advancement within the academy. There is a need for equity-minded research about these topics. In addition to replication across institutional and disciplinary contexts, there is also a need for longitudinal mixed-methods studies of midcareer faculty mentoring outcomes over time.Practical implicationsThe study points to the role of the institution and its senior faculty in fostering midcareer scholarly excellence. Mentoring and development around individualized goals may be of value in addition to an emphasis on clarity around institutional expectations and norms in faculty performance reviews.Originality/valueMidcareer faculty are a crucial component of the academy, yet they are often overlooked as needing career support, resources and development. This study focuses on mentoring and coaching for postsecondary faculty at midcareer and the role of exceptional others in facilitating faculty professional growth.

Cuticular chemical compounds of puparial cases of three forensically important blow flies from Egypt: potential for accurate identification and forensic investigations

AbstractThe distinct and species-specific chemical compounds found on the insect cuticle have demonstrated effectiveness in various applications, including species identification. Accurate identification of fly species becomes challenging when only damaged empty puparial cases are available, making it difficult to use traditional morphological and molecular identification methods. This study aimed to analyze the chemical compositions of puparial exuviae from three forensically and medically important fly species; Lucilia sericata, Chrysomya albiceps, and Chrysomya marginalis. Gas chromatography/mass spectrometry (GC–MS) was employed to assess the chemical profiles of these exuviae and evaluate their accuracy in identifying Dipteran insects. The study revealed the presence of twelve classes of chemical compounds across the three species, with retention times ranging from 18.78 to 35.03. A total of forty-two compounds with chain lengths ranging from C12 to C45 were identified. The profiles of Ch. albiceps and L. sericata displayed similarities, with alcohol being the most abundant compound (28.6%) in L. sericata. However, alkanes, including n-alkanes, branched alkanes, and cycloalkanes, constituted the main components of the cuticles in the three species, with Ch. marginalis displaying the highest percentage. These findings represent an initial step towards utilizing hydrocarbon composition as a practical tool for distinguishing between forensic species in Egypt.

Chiral acidic molecularly imprinted polymer for enantio-separation of norepinephrine racemate.

We are looking into how well a copolymeric material made of poly (maleic acid-co-4-vinylpyridine) cross-linked with divinylbenzene can separate L-norepinephrine (L-NEP) from (±)-NEP. The initial step in this direction was the synthesis and subsequent analysis of L-NEP-maleimide chiral derivative. A 4-vinylpyridine/divinylbenzene combination was copolymerized with the resultant chiral maleimide. After heating the polymer materials in a high-alkaline environment to breakdown the connecting imide bonds, they were acidified in an HCl solution to eliminate the incorporated L-NEP species. Fourier transform infrared spectroscopy (FTIR) and a scanning electron microscope were used to examine the imprinted L-NEP-imprinted materials. The manufactured L-NEP-imprinted materials exhibited selectivity characteristics that were over 11 times greater for L-NEP than D-norepinephrine. The highest capacity observed in Langmuir adsorption studies was 170 mg/g at a pH of 7. After optical separation using a column technique, it was determined that the enantiomeric excess levels of D-norepinephrine and L-NEP in the first feeding and subsequent recovery solutions were 95% and 81%, respectively.

Improved thermal network model for hot spot location in traction transformer windings

Obtaining accurate location information of the hot spot in advance is a prerequisite for locating the hot spot. To tackle this problem, we propose an improved thermal network model, which provides a quantitative method for placing fiber optic sensors at hot-spot locations. The initial step is to construct an enhanced thermal network model that takes into account the thermal resistance of the boundary layer using heat transfer theory. Then, we compare this model with Computational Fluid Dynamics (CFD) in example calculations to validate its effectiveness and precision. The results show that our proposed model can accurately and rapidly calculate temperature distribution within winding areas, enabling the identification of hot spots. This information is reliable for installing monitoring equipment to measure transformer temperatures.

Barriers to Physical Activity (PA) in the Working Population: A Review

Regular physical activity (PA) plays a crucial role in promoting overall health and preventing non-communicable diseases, with exercise both during and outside working hours reducing accumulated fatigue and stress caused by various job tasks. However, despite this, many people do not engage in sufficient levels of PA for various reasons. In line with this, this comprehensive research aimed to identify the barriers to physical exercise among the working population (WP). A search of literature was conducted using the following databases: Google Scholar, PubMed, Medline, and Mendeley, covering the period from 2002 to 2022. To potentially include in the study, all titles and abstracts were reviewed. A search of the databases based on keywords yielded 420 studies, then, through analysis and in accordance with the study’s objectives, 50 studies were included for analysis. Through a detailed analysis of selected studies, various barriers to PA in the WP have been identified. All barriers can be divided into three main groups: i) Work schedule and workplace as barriers; ii) Marital and extramarital relationships, parenthood, family, and household obligations as barriers; iii) Time, behavioral, socio-economic, demographic, and ecological factors as barriers. The detected barriers suggest that interventions to overcome them should not be directed toward a single solution, given their diversity. In the context of the contemporary lifestyle where economic activities almost dominantly prevail, maintaining the health and adequate work level of employees is of utmost importance, further emphasizing the significance of PA and the identification of barriers of various kinds. Therefore, this research is of great value, as by determining the state and identifying different barriers to PA, it can serve as an initial step in devising various measures and solutions to eliminate these barriers and enable the WP to engage in physical activities for the improvement of their health and work potential.

A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection

The selection of critical features from microarray data as biomarkers holds significant importance in disease diagnosis and drug development. It is essential to reduce the number of biomarkers while maintaining their performance to effectively minimize subsequent validation costs. However, the processing of microarray data often encounters the challenge of the “curse of dimensionality”. Existing feature-selection methods face difficulties in effectively reducing feature dimensionality while ensuring classification accuracy, algorithm efficiency, and optimal search space exploration. This paper proposes a hybrid feature-selection algorithm based on an enhanced version of the Max Relevance and Min Redundancy (mRMR) method, coupled with differential evolution. The proposed method improves the quantization functions of mRMR to accommodate the continuous nature of microarray data attributes, utilizing them as the initial step in feature selection. Subsequently, an enhanced differential evolution algorithm is employed to further filter the features. Two adaptive mechanisms are introduced to enhance early search efficiency and late population diversity, thus reducing the number of features and balancing the algorithm’s exploration and exploitation. The results highlight the improved performance and efficiency of the hybrid algorithm in feature selection for microarray data analysis.

Atmospheric reductive catalytic fractionation of lignocellulose integrated with one-pot catalytic conversion of carbohydrate yielding valuable lignin monomers and platform chemicals from corn straw

Developing a cost-effective and environmentally friendly process for the production of valuable chemicals from abundant herbal biomass receives great attentions in recent years. Herein, taking advantage of the “lignin first” strategy, corn straw is converted to valuable chemicals including lignin monomers, furfural and 5-methoxymethylfurfural via a two steps process. The key of this research lies in the development of a green and low-cost catalytic process utilizing magnetic Raney Ni catalyst and high boiling point ethylene glycol. The utilization of neat ethylene glycol as the sole reactant under atmospheric conditions obviates the need for additional additives, thereby facilitating the entire process to be conducted in glass flasks and rendering it highly convenient for scaling up. In the initial step, depolymerization of corn straw lignin resulted in a monomer yield of 18.1 wt%. Subsequently, in a dimethyl carbonate system, the carbohydrate component underwent complete conversion in a one-pot process, yielding furfural and 5-methoxymethylfurfural as the primary products with an impressive yield of 47.7 %.

Electron Density Specification in the Inner Magnetosphere From the Narrow Band Receiver Onboard DSX

AbstractElectron density plays an important role in the study of wave propagation and is known to be associated with the index of refraction and radiation belt diffusion coefficients. The primary objective of our investigation is to explore the possibility of implementing an onboard signal processing algorithm to automatically obtain electron densities from the upper hybrid resonance traces of wave spectrograms for future missions. U‐Net, developed for biomedical image segmentation, has been adapted as our deep learning architecture with results being compared with those extracted from a more traditional semi‐automated method. As a product, electron densities and cyclotron frequencies for the entire DSX mission between 2019 and 2021 are acquired for further analysis and applications. Due to limited space measurements, a synthetic image generator based on data statistics and randomization is proposed as an initial step toward the development of a generative adversarial network in hopes of providing unlimited realistic data sources for advanced machine learning.

Effects of Cognitive Inhibition Preceding Voluntary Step Responses to Visual Stimuli in Young and Older Adults.

Age-related changes in executive functions, especially inhibitory control, correlate to decreased balance control and increased fall risk. However, only a few studies focused on the performance of tasks integrating balance and inhibitory control. This study aims to determine the effects of cognitive inhibition preceding the initiation of voluntary steps in young and older adults. Performance of 3 stepping tasks (a Simon, Flanker, and a combined Simon-Flanker task [SFT]) were analyzed in 23 young adults and 43 older adults. Each task included congruent and incongruent trials in different step directions. Analyses focused on temporal aspects of step responses as identified by changes in Center of Pressure (CoP) and foot position. A 3-way repeated-measures ANOVA was used to evaluate "inhibition," "age," and "task" effects. With large effect sizes, "inhibition" as well as "age" resulted in longer durations of an initial preparatory phase as well as the step response phase. The SFT showed the largest "task" effects. Duration of CoP movement had the largest impact on total step execution in older adults. A significant interaction effect of "age*inhibition" was found on the duration of CoP movement, but not on CoP onset. Overall, our results demonstrate that cognitive inhibition has more impact in older adults, the longer duration of CoP movements in older adults may reflect an ineffective step preparation. Our examination of the duration of subsequent phases which comprise perceptual processing and conflict resolution, response initiation, and step execution sheds light on how cognitive inhibition affects voluntary stepping behavior in young and older adults.

Construction of a Cu@hollow TS‐1 nanoreactor based on a hierarchical full‐spectrum solar light utilization strategy for photothermal synergistic artificial photosynthesis

AbstractThe artificial photosynthesis technology has been recognized as a promising solution for CO2 utilization. Photothermal catalysis has been proposed as a novel strategy to promote the efficiency of artificial photosynthesis by coupling both photochemistry and thermochemistry. However, strategies for maximizing the use of solar spectra with different frequencies in photothermal catalysis are urgently needed. Here, a hierarchical full‐spectrum solar light utilization strategy is proposed. Based on this strategy, a Cu@hollow titanium silicalite‐1 zeolite (TS‐1) nanoreactor with spatially separated photo/thermal catalytic sites is designed to realize high‐efficiency photothermal catalytic artificial photosynthesis. The space–time yield of alcohol products over the optimal catalyst reached 64.4 μmol g−1 h−1, with the selectivity of CH3CH2OH of 69.5%. This rationally designed hierarchical utilization strategy for solar light can be summarized as follows: (1) high‐energy ultraviolet light is utilized to drive the initial and difficult CO2 activation step on the TS‐1 shell; (2) visible light can induce the localized surface plasmon resonance effect on plasmonic Cu to generate hot electrons for H2O dissociation and subsequent reaction steps; and (3) low‐energy near‐infrared light is converted into heat by the simulated greenhouse effect by cavities to accelerate the carrier dynamics. This work provides some scientific and experimental bases for research on novel, highly efficient photothermal catalysts for artificial photosynthesis.

Awareness of the Seismic Effect on Existing High-rise Building in Georgetown Penang

A seismic event is unpredictable and may occur worldwide depending on the movement of the fault line. The impact of this phenomenon on society, structures, sources and surroundings is significant to be investigated. Thus, this study aims to investigate the seismic awareness level and input information among Malaysians. Several far-field seismic activities in Malaysia have triggered some high-rise buildings, including residential and commercial ones. Due to this situation, the occupants started to worry about their safety living in high-rise buildings designed without seismic considerations. The most important initial step is to investigate the awareness level among the occupants. A survey has been conducted in Georgetown, Pulau Pinang area involving 208 respondents. Findings indicate the respondents have limited experience with seismic events in Malaysia and were never exposed to seismic information. Less than 50% declared to have adequate knowledge about seismic. Only 31.25% of the respondents were exposed to the seismic information. Respondents agree that Malaysia should apply a seismic code of practice for all construction buildings. Most respondents believe that preparation for upcoming seismic activities is still insufficient, and all new buildings should have seismic safety certificates in the future for seismic resistance.

Ab Initio and Kinetic Modeling of β-d-Xylopyranose under Fast Pyrolysis Conditions.

Lignocellulosic biomass is an abundant renewable resource that can be upgraded to chemical and fuel products through a range of thermal conversion processes. Fast pyrolysis is a promising technology that uses high temperatures and fast heating rates to convert lignocellulose into bio-oils in high yields in the absence of oxygen. Hemicellulose is one of the three major components of lignocellulosic biomass and is a highly branched heteropolymer structure made of pentose, hexose sugars, and sugar acids. In this study, β-d-xylopyranose is proposed as a model structural motif for the essential chemical structure of hemicellulose. The gas-phase pyrolytic reactivity of β-d-xylopyranose is thoroughly investigated using computational strategies rooted in quantum chemistry. In particular, its thermal degradation potential energy surfaces are computed employing Minnesota global hybrid functional M06-2X in conjunction with the 6-311++G(d,p) Pople basis set. Electronic energies are further refined by performing DLPNO-CCSD(T)-F12 single-point calculations on top of M06-2X geometries using the cc-pVTZ-F12 basis set. Conformational analysis for minima and transition states is performed with state-of-the-art semiempirical quantum chemical methods coupled with metadynamics simulations. Key thermodynamic quantities (free energies, barrier heights, enthalpies of formation, and heat capacities) are computed. Rate coefficients for the initial steps of thermal decomposition are computed by means of reaction rate theory. For the first time, a detailed elementary reaction kinetic model for β-d-xylopyranose is developed by utilizing the thermodynamic and kinetic information acquired from the aforementioned calculations. This model specifically targets the initial stages of β-d-xylopyranose pyrolysis in the high-pressure limit, aiming to gain a deeper understanding of its reaction kinetics. This approach establishes a systematic strategy for exploring reactive pathways, evaluating competing parallel reactions, and selectively accepting or discarding pathways based on the analysis. The findings suggest that acyclic d-xylose plays a significant role as an intermediary in the production of key pyrolytic compounds during the pyrolysis of xylose. These compounds include furfural, anhydro-d-xylopyranose, glycolaldehyde, and dihydrofuran-3(2H)-one.

The Role of an Endodontist in Victim Identification: A Narrative Review on Forensic Endodontics.

This narrative review highlights the role of endodontists and the significance of various dental tools in forensic dentistry. An online search was conducted in peer-reviewed journals, including MEDLINE (Ovid), PubMed, Web of Science, Scopus, and Google Scholar databases, to retrieve studies regarding "the role of an endodontist in victim identification using different tools". The searches used controlled vocabulary and free-text terms. Articles written in English and published from 1923 to 2023 were selected. An essential stage in forensic dentistry is dental identification of the dead person and is regarded as an initial step for both judicial and humanitarian purposes if fingerprint records are missing or the remains have undergone significant changes. Endodontists should be aware of all available dental tools that aid in identification. The four fundamental tools for identification are dental radiographs, hard and soft dental structures, and dental materials. Dental radiographs provide a substantial nondestructive record for estimating age and sex. Moreover, maxillofacial hard and soft structures provide important tools for individual identification as they are considered the strongest structures in the human body and can withstand severe chemical and temperature changes. In addition, endodontic and restorative materials can be identified under different conditions and serve as excellent forensic identification measures.

Perceptions of Stakeholders on the Financial and Economic Crisis in Lebanon: an in-Depth Analysis

Purpose: The objective of this study is to delve into the root causes of Lebanon's severe economic crisis. This crisis has been exacerbated by a confluence of factors, including the impact of the COVID-19 pandemic, pre-existing economic difficulties, and the devastating Beirut explosion. The aim is to uncover the fundamental drivers behind the crisis, which encompass issues such as financial mismanagement, governmental shortcomings, excessive debt burdens, intricate financial engineering practices, and political unrest. Methodology: Employing a quantitative methodology, this research leverages a survey of 178 pertinent stakeholders. The approach encompasses both descriptive analysis and factor analysis. Descriptive statistics were calculated for each variable, providing a comprehensive overview of the perspectives held by the participants. This initial step then guided the subsequent process of factor analysis, which was executed utilizing Principal Component Analysis (PCA) as the technique. Findings: The results disclose a complex array of factors that have collectively contributed to the current economic turmoil in Lebanon. The presence of a dominant underlying component indicates a strong association between variables related to financial mismanagement, government failures, public debt, financial engineering techniques, and political instability. These factors play a substantial role in driving the crisis, indicating an intricate web of interrelated elements contributing to the country's financial downturn. Research Limitations/Implications: While this study provides valuable insights into the origins of the crisis, it is important to acknowledge certain limitations. The research primarily relies on quantitative data and stakeholder surveys, which may not capture the entirety of the complex socio-political and economic dynamics at play. Additionally, the study does not delve deeply into potential geopolitical factors that may also be influencing the crisis. Originality/Value: This research contributes to the existing body of knowledge by offering a data-backed analysis of the Lebanon economic crisis. The identification of specific drivers such as financial mismanagement, governmental failures, and political unrest provides a foundation for targeted policy recommendations. The study emphasizes the importance of implementing prudent fiscal practices, governance reforms, and tailored policies to address the crisis and prevent similar situations in the future.

Social capital development on interest-based networks: examining its antecedents, process, and consequences

Social capital development has been extensively studied on relationship-based networks (i.e., Facebook, WeChat), whereas scant attention has been devoted to another critical category: interest-based networks. People join interest-based networks primarily to exchange information on shared topics and interests, as opposed to relationship development or maintenance. Guided by social capital theory, the current research takes an initial step by looking into whether and how bridging and bonding social capital accrues on interest-based networks. A cross-sectional survey was conducted among users of a well-known Chinese interest-based network, Douban.com (N = 624). The participants reported their bridging and bonding social capital, frequency of online interactions with content (i.e., posting, favoriting) and with humans (i.e., chatting, discussing), and sense of belonging. The mediation analysis results informed us of a theoretical model of social capital development on interest-based networks with both online interactions with content and with humans as the antecedents, and a sense of belonging as the consequence. The findings also revealed the steps of bridging and bonding social capital development on interest-based networks. This study advanced our understanding of social capital theory by examining its applicability in an interest-based SNS, identifying OIs with humans and content as potential antecedents, and elucidating the association between two types of social capital. The findings hold practical significance for designers of interest-based networks.

Crosstalk Between NK Cell Receptors and Tumor Membrane Hsp70-Derived Peptide: A Combined Computational and Experimental Study.

Natural killer (NK) cells are central components of the innate immunity system against cancers. Since tumor cells have evolved a series of mechanisms to escape from NK cells, developing methods for increasing the NK cell antitumor activity is of utmost importance. It is previously shown that an ex vivo stimulation of patient-derived NK cells with interleukin (IL)-2 and Hsp70-derived peptide TKD (TKDNNLLGRFELSG, aa450-461) results in a significant upregulation of activating receptors including CD94 and CD69 which triggers exhausted NK cells to target and kill malignant solid tumors expressing membrane Hsp70 (mHsp70). Considering that TKD binding to an activating receptor is the initial step in the cytolytic signaling cascade of NK cells, herein this interaction is studied by molecular docking and molecular dynamics simulation computational modeling. The in silico results showed a crucial role of the heterodimeric receptor CD94/NKG2A and CD94/NKG2C in the TKD interaction with NK cells. Antibody blocking and CRISPR/Cas9-mediated knockout studies verified the key function of CD94 in the TKD stimulation and activation of NK cells which is characterized by an increased cytotoxic capacity against mHsp70 positive tumor cells via enhanced production and release of lytic granules and pro-inflammatory cytokines.

Rab11 regulates autophagy at dendritic spines in an mTOR- and NMDA-dependent manner.

Synaptic plasticity is a process that shapes neuronal connections during neurodevelopment and learning and memory. Autophagy is a mechanism that allows the cell to degrade its unnecessary or dysfunctional components. Autophagosomes appear at dendritic spines in response to plasticity-inducing stimuli. Autophagy defects contribute to altered dendritic spine development, autistic-like behavior in mice, and neurological disease. While several studies have explored the involvement of autophagy in synaptic plasticity, the initial steps of the emergence of autophagosomes at the postsynapse remain unknown. Here, we demonstrate a postsynaptic association of autophagy-related protein 9A (Atg9A), known to be involved in the early stages of autophagosome formation, with Rab11, a small GTPase that regulates endosomal trafficking. Rab11 activity was necessary to maintain Atg9A-positive structures at dendritic spines. Inhibition of mTOR increased Rab11 and Atg9A interaction and increased the emergence of LC3 positive vesicles, an autophagosome membrane-associated protein marker, in dendritic spines when coupled to NMDA receptor stimulation. Dendritic spines with newly formed LC3+ vesicles were more resistant to NMDA-induced morphologic change. Rab11 DN overexpression suppressed appearance of LC3+ vesicles. Collectively, these results suggest that initiation of autophagy in dendritic spines depends on neuronal activity and Rab11a-dependent Atg9A interaction that is regulated by mTOR activity.

A new approach for atmospheric turbulence removal using low-rank matrix factorization.

In this article, a novel method for removing atmospheric turbulence from a sequence of turbulent images and restoring a high-quality image is presented. Turbulence is modeled using two factors: the geometric transformation of pixel locations represents the distortion, and the varying pixel brightness represents spatiotemporal varying blur. The main framework of the proposed method involves the utilization of low-rank matrix factorization, which achieves the modeling of both the geometric transformation of pixels and the spatiotemporal varying blur through an iterative process. In the proposed method, the initial step involves the selection of a subset of images using the random sample consensus method. Subsequently, estimation of the mixture of Gaussian noise parameters takes place. Following this, a window is chosen around each pixel based on the entropy of the surrounding region. Within this window, the transformation matrix is locally estimated. Lastly, by considering both the noise and the estimated geometric transformations of the selected images, an estimation of a low-rank matrix is conducted. This estimation process leads to the production of a turbulence-free image. The experimental results were obtained from both real and simulated datasets. These results demonstrated the efficacy of the proposed method in mitigating substantial geometrical distortions. Furthermore, the method showcased the ability to improve spatiotemporal varying blur and effectively restore the details present in the original image.

A novel assay to measure low-density lipoproteins binding to proteoglycans.

The binding of low-density lipoprotein (LDL) to proteoglycans (PGs) in the extracellular matrix (ECM) of the arterial intima is a key initial step in the development of atherosclerosis. Although many techniques have been developed to assess this binding, most of the methods are labor-intensive and technically challenging to standardize across research laboratories. Thus, sensitive, and reproducible assay to detect LDL binding to PGs is needed to screen clinical populations for atherosclerosis risk. The aim of this study was to develop a quantitative, and reproducible assay to evaluate the affinity of LDL towards PGs and to replicate previously published results on LDL-PG binding. Immunofluorescence microscopy was performed to visualize the binding of LDL to PGs using mouse vascular smooth muscle (MOVAS) cells. An in-cell ELISA (ICE) was also developed and optimized to quantitatively measure LDL-PG binding using fixed MOVAS cells cultured in a 96-well format. We used the ICE assay to show that, despite equal APOB concentrations, LDL isolated from adults with cardiovascular disease bound to PG to a greater extent than LDL isolated from adults without cardiovascular disease (p<0.05). We have developed an LDL-PG binding assay that is capable of detecting differences in PG binding affinities despite equal APOB concentrations. Future work will focus on candidate apolipoproteins that enhance or diminish this interaction.

Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values ​​for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%