The diagnosis of acute myeloid leukemia (AML) is frequently suspected and occasionally confirmed using peripheral blood examination. The current study aimed to evaluate the diagnostic methods used for AML diagnosis. This study included 360 newly diagnosed patients attending the Hematology Clinic at Baghdad Teaching Hospital, Medical City. The cohort included 147 men and 213 women, with a mean age of 45.8 years and an age range of 17–76 years. Diagnosis and classification were performed using peripheral blood smear examination, bone marrow aspiration, and flow cytometry. Flow cytometric immunophenotyping of bone marrow samples showed a distinct blast population on the CD45/side-scatter (SSC) dot plot, accounting for 70.0 ± 3.5% of gated events. The lowest blast percentage was 36%, observed in AML-M2 and AML-M6, whereas the highest blast percentage was 90%, observed in AML-M3. The most common FAB subtype in the AML group was AML-M0, accounting for 32% of cases, followed by AML-M3 at 26%. AML-M1 accounted for 7%, AML-M2 for 4%, AML-M4 for 12%, AML-M5 for 8%, AML-M6 for 5%, and AML-M7 for 7% of AML cases. A clear understanding of the pathological information required for AML diagnosis has become increasingly important for guiding treatment decisions and improving patient outcomes.
Pulsed laser ablation in liquid (PLAL) is a simple and rapid technique that enables the synthesis of nanoparticles (NPs) with high purity. It is a top-down physical approach based on the fragmentation of a bulk metal precursor. In this work, iron oxide nanoparticles (Fe3O4NPs) were synthesized using PLAL with an Nd laser at a pulse energy of 500 mJ . Two ablation wavelengths, 355 and 532 nm, and different pulse numbers, 500, 600, 700, 800, and 900 pulses, were used for both wavelengths to evaluate their effects on the optical properties of the synthesized NPs. Structural and morphological characterization was performed for the NPs synthesized using the highest number of pulses, 900 pulses, using several analytical techniques. The antibacterial activity of all synthesized NPs was then evaluated against four bacterial species isolated from the oral cavity: Streptococcus mutans, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli. The results showed that the synthesized NPs were spherical and exhibited a cubic polycrystalline phase, with average crystallite sizes of 17.47 and 20.39 nm for the samples synthesized at 355 and 532 nm , respectively. The lower ablation wavelength resulted in smaller average crystallite size, higher absorbance, and greater antibacterial activity compared with the longer wavelength. These findings indicate that Fe3O4 NPs synthesized by PLAL have antibacterial potential and may be considered for further investigation as supportive antimicrobial agents and for other biomedical applications.
The Traveling Salesman Problem (TSP) is one of the most prominent combinatorial optimization problems, attracting significant interest in both research and practical applications. This paper compares the performance of two approaches for solving the TSP: the Cheapest Insertion Heuristic (CIH) and the Branch-and-Bound (B&B) algorithm. The results show that CIH produced a route of 584 km. This approach is simple and fast to execute, but it provides approximate solutions and does not guarantee optimality. In contrast, B&B was effective in finding the optimal solution by relying on lower-bound (LB) calculations and systematically eliminating nonpromising branches. The best route obtained using B&B was A → D → C → B → E → A. The comparison suggests that CIH is more suitable for large-scale problems requiring quick solutions, whereas B&B is preferable when obtaining the optimal solution is essential, despite its higher computational cost and longer execution time.
Obesity occurs as a result of long-term positive energy balance, in which energy intake exceeds energy expenditure. This anabolic condition can lead to impaired hypothalamic neuronal activity involved in nutrient sensing and nutrient-signaling responses. Proopiomelanocortin (POMC) is a neuropeptide that plays a pivotal role in regulating appetite and neuroendocrine responses to energy balance. From this perspective, the study aimed to assess the impact of 6 months of lifestyle intervention on neurohormonal and inflammatory indicators, including POMC, leptin, and IL-17A, in participants with obesity. A quasi-experimental longitudinal controlled study involved 100 adults of both sexes: 50 individuals with obesity (30 males and 20 females) and 50 healthy controls (37 males and 13 females), classified based on body composition analysis using the InBody device. Participants with obesity received personalized instructions on healthy diet and physical activity throughout the 6-month intervention period. Neuroendocrine and inflammatory markers were assessed at baseline in both groups and reassessed after the intervention in participants with obesity. At baseline, participants with obesity showed significantly higher neuroendocrine and inflammatory marker levels compared with healthy controls. After 6 months of lifestyle intervention, these parameters decreased significantly among participants with obesity compared with their baseline values; however, they remained elevated compared with healthy controls. Lifestyle modification for 6 months significantly improved neuroendocrine and inflammatory markers in adults with obesity. To the best of our knowledge, this is the first study to investigate the impact of lifestyle intervention on POMC in adults with obesity, highlighting the neuroendocrine response to weight loss.
The identification of patterns in electrocardiograms (ECGs) involves analyzing ECG signals through deep learning (DL) or machine learning (ML) methods to recognize specific patterns associated with various heart conditions. This study focuses on diagnosing heart disease patterns using polygon approximation techniques to extract features from ECG signals, offering a novel approach that hasn’t been explored in the realm of ECG pattern recognition with machine learning classification methods. We evaluate two dominant point techniques. The first technique involves polygon approximation, and the second technique is based on curvature, implemented through the Ramer Douglas Peucker (RDP) algorithm and the Rosenfeld and Johnston (R&J) algorithm, respectively. This evaluation is conducted using two parameters: execution time and classification accuracy. We used several ML algorithms for the classification task, such as XGBoost, Random Forest (RF), Support Vector Machines (SVMs) with different kernels, and K-Nearest Neighbors (KNNs). RF classifier in conjunction with the Ramer Douglas Peucker approximation produced the most significant classification accuracy of 98.5%.
Obesity is an escalating health problem in developing countries and represents a major global public health concern associated with increased morbidity and mortality. This study aimed to evaluate obestatin, adipsin, and neuropeptide Y as potential biomarkers of obesity in men and to assess their diagnostic performance using receiver operating characteristic (ROC) curve analysis. The study included 90 male participants from Samarra city, who were divided according to body mass index into normal-weight control, overweight, and obese groups. The results showed that obestatin and glutathione levels were significantly decreased in overweight and obese individuals compared with normal-weight controls (p ≤ 0.05). In contrast, adipsin, neuropeptide Y, and malondialdehyde levels were significantly higher in overweight and obese individuals. Total cholesterol, triglycerides, low-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, and glucose levels were also elevated in the overweight and obese groups compared with the normal-weight group. ROC curve analysis showed strong diagnostic performance for the studied biomarkers. Obestatin achieved 100% sensitivity and 86.7% specificity, whereas neuropeptide Y showed excellent diagnostic accuracy, with both sensitivity and specificity reaching 100%. The present study concluded that obestatin levels were lower, while adipsin, neuropeptide Y, and malondialdehyde levels were higher in overweight and obese individuals than in normal-weight controls. These findings suggest that obestatin, adipsin, and neuropeptide Y may serve as promising biomarkers for obesity.
With advances in technology, handling video files is of paramount importance, especially in the educational, medical, and security fields. Various applications have been developed to understand, present, distinguish, retrieve, extract semantic meaning, or create video using current artificial intelligence technologies. One of the most recent and important technologies is the vision transformer, developed to accurately handle video. In this paper, we discuss the most important developments in this intelligent technology and its main applications over the past few years. In this paper, we present a comprehensive comparative review of the most prominent visual transformer models used in video processing, with an in-depth analysis of their architectures, identification of their applications, and discussion of their advantages and disadvantages. What distinguishes this work from previous reviews is its focus on classifying transformer-based model types, linking them to computational complexity, the type of attention used, and performance across various tasks. This paper also includes analytical tables linking each model to the dataset used and the results achieved, highlights current research gaps, and offers informed future recommendations for developing these models to serve advanced smart applications.
Kummer’s equation has been used in many physical, engineering, and scientific applications, including the study of the Boltzmann energy distribution, the hydrogen atom through transformation of the radial equation into Kummer’s equation, electrical-circuit modeling, and the solution of Laplace’s equation in circular and cylindrical coordinates. This paper presents an alternative solution to Kummer’s differential equation based on the Dirac delta distribution using the Fourier transform method (FTM). The proposed solution does not depend on Kummer’s confluent hypergeometric function or Gordon’s function. In addition, the Frobenius method was employed to solve the differential equation obtained after applying the Fourier transform method. The validity and effectiveness of the proposed method are demonstrated. However, in certain cases, classical solutions may be insufficient to describe singular phenomena or may fail to exist within standard function spaces. This motivates the study of generalized solutions formulated within the framework of distribution theory.
This work presents a rare discovery of the Panaeolina foenisecii (Pers.) Maire (1913) mushroom (Family: Galeropsidaceae) in the Khazraj village, located west of Heet city in western Iraq. The collection took place in December 2018, and regional weather data showed fluctuating temperatures and moderate precipitation, which likely contributed to the emergence of the mushroom’s fruiting bodies. The morphological characteristics, including cap shape, gill structure, and stem features, were analyzed to aid in species identification. A microscopic examination of the spores confirmed their typical almond-shaped, small size, dark brown to black color, and smooth surface, consistent with the species of Panaeolina foenisecii. The findings underscore the importance of environmental factors in the growth of these mushrooms and contribute to the understanding of their distribution in arid regions, such as western Iraq. This mushroom may cause hallucinations; therefore, its consumption is not recommended. Finally, P. foenisecii is considered the first record in Iraq, and it is very useful for enriching Iraqi mycodata.
The Emden − Fowler equation (𝐸 − 𝐹 − Eq) is used in math and other sciences lik chemistry, astrophysics and physics. It can also be changed to become the Lane- Emden equation (L_E_Ed) by using a specific function, and it is applied in various scientific fields along with math. Many researchers examine different ways, both analytical and numerical, to solve these types of equations, whether they are linear or nonlinear. Using artificial intelligence and machine learning, we can now find solution to complicated equations more quickly and accurately than with traditional methods. The goal of this study is solution (𝐸 − 𝐹 − Eq) singular second order differential equations via neural network by three main steps. First, a neural network (A-N-N) setup (a type of algorithm) is created to mimic the problem being worked on. Next, this algorithm is used to find a numerical solution for (𝐸 − 𝐹 − Eq). Finally, the results are compared with well-known approximate answers from various examples, using tables and figures for specific parameters to show the accuracy and effectiveness of the ANN method.
In this work, nanostructured tin oxide thin films (SnO2) were deposited on silicon substrates at room temperaturIn this work, nanostructured tin oxide (SnO2) thin films were deposited on silicon substrates at room temperature using pulsed laser deposition (PLD). The study focused on the effect of mixing SnO2 films with europium oxide (Eu2O3) at different weight ratios of 5, 7, and 9 wt% on the gas-sensing properties of the prepared films. X-ray diffraction (XRD) analysis revealed that the addition of Eu2O3 contributed to the formation of separate SnO, SnO2, and Eu2O3 phases, which significantly improved nitrogen dioxide (NO2) sensing performance. Surface morphology analysis showed that particle distribution varied with increasing Eu2O3 content. In particular, the sample containing 7 wt% Eu2O3 exhibited a narrower particle size distribution and more homogeneous particle growth on the sensor surface, resulting in the highest sensitivity among the prepared samples. The thickness of the deposited films increased with increasing Eu2O3 content, although a slight decrease in thickness was observed for the sample containing 5 wt% Eu2O3. Gas-sensing results against different NO2 concentrations showed that the optimum operating temperature was 200 °C. The SnO2 film mixed with 7 wt% Eu2O3 exhibited the highest sensitivity of 626% at 120 ppm NO2, with a response time of 10 s and a recovery time of 12 s. These findings indicate that Eu2O3 mixing can enhance the sensing properties of SnO2 by modifying its nanostructure and surface morphology, increasing oxygen-vacancy concentration, and improving sensitivity under different environmental conditions.e using pulsed laser deposition (PLD). The study focused on the effect of mixing SnO2 films with europium oxide (Eu2O3) at varying weight ratios (5, 7, and 9 wt%) on the gas sensing properties of the developed films. X-ray diffraction (XRD) revealed that the addition of Eu2O3 contributed to the growth of separate phases of SnO, SnO2, and Eu2O3, which significantly improved the sensitivity of nitrogen dioxide (NO2) sensing. The surface morphology of the deposited films revealed a distribution that varied with increasing Eu2O3 content. In contrast, the sample with a 7 wt% Eu2O3 content showed a narrower particle size distribution and more homogeneous particle growth on the sensor surface, which in turn demonstrated the highest sensitivity among the prepared samples. The thickness of the deposited films increased with increasing Eu2O3 mixing ratios, while a slight dip in thickness was observed for the sample combined with 5 wt% Eu2O3. Sensitivity results of the developed films against varying NO2 concentrations demonstrated that the optimum operating temperature was 200°C. The SnO2 mixed with 7 wt% Eu2O3 revealed the highest sensitivity (626%) at 120 ppm, with a response time of 10 seconds and a recovery time of 12 seconds. Eu2O3 mixing holds promise for enhancing the sensor properties of SnO2 by modifying its nanostructure and surface morphology, improving the oxygen vacancy concentrations, and increasing sensitivity under various environmental conditions.
Celiac disease is an autoimmune-mediated disorder triggered by gluten consumption in genetically predisposed individuals, leading to villous atrophy and malabsorption. Although duodenal biopsy remains a key diagnostic tool, the role of heat shock proteins (HSPs) in mucosal injury and recovery has not been fully clarified. This study aimed to examine the relationship between celiac disease-specific autoantibodies and the expression levels of heat shock proteins (HSP70, HSP72, and HSP75) in patients with celiac disease and to correlate these findings with histopathological grades (Marsh 3A–3C). This case-control study included 64 newly diagnosed patients with celiac disease and 26 non-celiac controls from Al-Anbar Governorate, Iraq. Serum anti-tissue transglutaminase antibodies (tTG-IgA and tTG-IgG), anti-gliadin antibodies (IgA and IgG), and anti-deamidated gliadin peptide antibodies (DGP-IgA and DGP-IgG) were measured by ELISA using the Chorus TRIO system. Serum HSP70 and HSP72 levels were measured by ELISA, whereas HSP70, HSP72, and HSP75 expression in duodenal biopsies was assessed by immunohistochemistry (IHC) according to the Marsh–Oberhuber classification. Statistical analysis was performed using SPSS version 29.0, with significance set at p < 0.05. All measured autoantibodies and serum HSP markers were significantly higher in patients with celiac disease than in controls (p < 0.05). The severity of mucosal damage was associated with increasing antibody titers, with the highest levels observed in Marsh 3C cases. Serum HSP70 and HSP72 levels were generally increased in patients with celiac disease; however, they did not differ significantly among the Marsh subgroups. IHC analysis showed that HSP70 and HSP72 expression decreased with increasing villous atrophy, while HSP75 expression showed no significant trend. Celiac disease-specific autoantibodies were strongly associated with histological severity, supporting their use as practical non-invasive biomarkers. Changes in HSP70 and HSP72 expression suggest their involvement in mucosal stress responses and disease progression, indicating their potential value as future therapeutic or predictive biomarkers in celiac disease.
Air pollution is a critical threat to environmental sustainability and public health, particularly in densely populated countries such as India and Nepal. This paper presents a hybrid pipeline that combines deep learning feature extraction with traditional machine learning classification for image-based air quality assessment. The method employs a two-stage pipeline: feature extraction using a pre-trained VGG16 CNN and classification using SVM and Random Forest (RF) algorithms. The model breaks down air quality into six levels: Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, and Severe. A publicly available dataset of photographs taken at multiple urban sites in India and Nepal was used to conduct the test. Experimental results show that the Random Forest classifier achieves 97 percent accuracy, compared to 95 percent for the SVM classifier, on the test data. The effectiveness of the method is confirmed by a large-scale evaluation based on precision, recall, F1-score, and ROC-AUC metrics. The findings indicate a significant potential for combining deep feature extraction with traditional machine learning algorithms to create scalable environmental monitoring systems for real-world applications.
Trace elements, such as copper and iron, play vital roles in brain metabolism and oxidative stress regulation. This study investigated the role of trace element imbalance, specifically copper and iron, in traumatic brain injury (TBI) severity, type, treatment, and patient outcomes over 3 months. A case-control study was conducted on 60 patients with TBI who were admitted to hospitals in Baghdad, Iraq, within 8 h of injury, along with 40 healthy controls. Serum samples were analyzed using colorimetric methods. Patient outcomes were assessed 3 months after injury in 31 patients. The results revealed significant differences in serum copper and iron levels between patients with TBI and healthy controls. Copper levels also differed significantly between the acute phase and 3 months after injury (p = 0.001), whereas iron levels increased but not significantly (p = 0.788). Copper levels were notably lower in patients with severe injuries than in those with moderate and mild injuries (p = 0.001). Mean copper levels were significantly lower in patients with poor outcomes than in those with good outcomes (p = 0.002). The ability of copper levels to discriminate patients with TBI was evaluated using receiver operating characteristic analysis, with an AUC of 0.242 (95% confidence interval [CI], 0.124–0.361). The optimal cutoff value was 91.65 µg/dL, with 11.4% specificity and 91.1% sensitivity. The AUC for iron was 0.342 (95% CI, 0.222–0.462). The optimal cutoff value was 92.3 µg/dL, with 79.4% specificity and 18.2% sensitivity. These findings suggest that altered copper and iron homeostasis may be associated with TBI and may reflect injury severity and patient outcomes.
Infection with Entamoeba histolytica and Helicobacter pylori is considered among the most common and serious infectious diseases, particularly in developing countries. The current study aimed to evaluate the prevalence of E. histolytica among patients with H. pylori infection and gastrointestinal symptoms in Mosul city and to study the phylogenetic relationships of the isolated strains. A total of 200 tissue biopsies were collected from 100 individuals of both sexes who had abdominal pain accompanied by gastrointestinal symptoms, including 100 gastric biopsies and 100 duodenal biopsies, from August 1, 2024, to March 1, 2025. The samples were examined using histological examination and conventional polymerase chain reaction (PCR), respectively, to identify E. histolytica in patients with positive H. pylori infection. The current study showed that the prevalence of H. pylori infection was 76%. Furthermore, parasite identification test results showed a prevalence of 26.66% for E. histolytica. Genetic analysis of E. histolytica was conducted, and two new strains were isolated and registered at the National Center for Biotechnology Information (NCBI) under accession numbers PV151461 and PV132085. The phylogenetic tree analysis of the locally isolated E. histolytica strains, which were recorded in GenBank, demonstrated high similarity (100%) between the two strains identified in the current study. Strain PV151461 showed high similarity with Iraqi strains from Tikrit and Diwaniya, OL771810 and ON086988, respectively. Isolate PV132085 showed a significant match with the Iraqi isolates obtained from Tikrit.
The study included the design of a treatment unit using Spirogyra sp. algae for industrial wastewater from the developed industrial area in Kirkuk City, Iraq. The use of algae in the bioremediation of industrial wastewater is considered one of the best treatment technologies because it is safe, inexpensive, and based on materials naturally available in the environment. The results showed that the algal filter containing Spirogyra sp. improved the wastewater quality, bringing the treated water within the permissible Iraqi standards for irrigation. Regarding heavy metals, the highest removal efficiency was recorded for cadmium (Cd), at 98.7%, followed by lead (Pb), at 98.6%, whereas the lowest removal efficiency was recorded for iron (Fe), at 85.33%, indicating a clear decrease in heavy metal concentrations. The sodium adsorption ratio decreased from 2.69 mEq/L before treatment to 1.99 mEq/L after treatment. Sodium percentage (Na%) decreased from 20% before treatment, when the water was classified as good for irrigation, to 17% after treatment, when the water was classified as excellent for irrigation. The magnesium adsorption ratio of the industrial wastewater was 52.88 mEq/L before treatment, indicating that the water was unsuitable for irrigation. After treatment, it decreased to 47.50 mEq/L, allowing the water to be classified as suitable for irrigation. The Kelly ratio decreased from 0.27 mEq/L before treatment to 0.22 mEq/L after treatment. Potential salinity decreased from 21.57 mEq/L before treatment to 7.17 mEq/L after treatment. Overall, the Spirogyra sp. algal filter showed high efficiency in treating industrial wastewater and improving its properties for potential reuse in irrigation.
We perform a comprehensive numerical study of how variations in microscopic coupling strength affect critical behavior in the two-dimensional Ising model. Using extensive Monte Carlo simulations based on the Wolff cluster algorithm, we investigate the clean Ising model on a square lattice for interaction strengths in the range 0.80 ≤ J ≤ 1.20. While the exact solution at J = 1 has long established the model’s critical properties, a systematic numerical assessment of universality under continuous coupling rescaling has not been performed. Here, we address this gap by analyzing systems of linear size L = 20 − 500, allowing for high-precision finite-size scaling of thermodynamic observables. For each coupling, the critical temperature T_C(J) is determined via finite-size extrapolation of the susceptibility maxima, yielding results in quantitative agreement with Onsager’s exact prediction. At the extrapolated critical points, we extract the critical exponents ν, β, γ, and α through detailed scaling analyses. These exponents remain consistent with the exact Ising benchmarks across the full range of interaction strengths, confirming that the universal aspects of the phase transition are preserved. Our findings demonstrate that modifying the microscopic coupling strength rescales the critical temperature without altering the asymptotic critical behavior, thereby providing a clear and quantitative confirmation of the robustness of the two-dimensional Ising universality class.
In this study, Cu-doped tin dioxide (SnO₂) thin films were successfully synthesized using the thermochemical decomposition method at a deposition temperature of 350 °C and a precursor concentration of 0.02 M. Copper was introduced at doping levels of 3%, 5%, and 7% to systematically investigate its effects on the structural, morphological, electrical, and gas-sensing characteristics of the SnO₂ films. X-ray diffraction (XRD) confirmed the retention of the tetragonal rutile structure in all samples, with Cu incorporation leading to a reduction in crystallite size and peak shifts indicative of strain and defect formation. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) analyses revealed progressive grain refinement and increased surface roughness with increasing Cu content. Hall-effect measurements demonstrated that Cu doping enhanced carrier mobility and electrical conductivity, with the 5% Cu-doped film exhibiting the most favorable transport properties. Room-temperature gas-sensing measurements using ammonia (NH₃) showed that the 5% Cu-doped SnO₂ film achieved the highest response, with a sensor response (Ra//Rg) of approximately 3.4 at 150 ppm, a response time of 22 s, a recovery time of 35 s, and excellent cyclic stability. Compared with the undoped film, this result corresponds to an estimated 160% improvement in sensing performance. These findings highlight the potential of moderately Cu-doped SnO₂ thin films for use in high-performance NH₃ sensors operating at room temperature (25 °C).
The Compton profiles of intra- and inter-shell interactions in the carbon atom were calculated and analyzed using the Roothaan-Hartree-Fock method. To examine the effect of increasing nuclear charge, the study was extended to include selected carbon-like ions: N⁺, O²⁺, and F³⁺. All calculations were performed for the ground state and reported in atomic units. A progressive narrowing of the orbitals with increasing nuclear charge appears to reduce the height of the central peak and extend the momentum distribution toward higher momentum values. The crossover points between the profiles were also identified, revealing regions in which the momentum density expands in response to ionization. The Compton profile of intra-shell interactions appears to contain two main regions: one near q = 0 and another at intermediate q values. For inter-shell interactions, more than one region was observed, reflecting the contribution of exchange processes. Overall, the findings are in good agreement with the published literature.
This study investigated the use of a new adsorbent material, chemically treated grape leaves, for the adsorption of the food dye E110 at temperatures of 298, 303, 308, 313, and 318 K. The removal efficiency ranged from 74.934% to 95.704%. Adsorption behavior was evaluated by determining the constants of several adsorption isotherm models, including Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich models. According to Beer–Lambert’s law, a standard calibration curve for E110 dye was prepared, and the residual dye concentration in solution was determined using UV–visible spectrophotometry. Thermodynamic functions were calculated from the adsorption isotherm constants. The positive Δ𝐻 values indicated that the adsorption of E110 dye was an endothermic process, while the thermodynamic results suggested that the adsorption was mainly physical in nature. The positive Δ𝐺° values indicated that the adsorption process was non-spontaneous under the studied conditions, although it tended toward greater spontaneity with increasing temperature. The positive Δ𝑆 and Δ𝑆° values indicated increased randomness at the solid–solution interface during adsorption. The higher Δ𝑆 values compared with Δ𝑆° suggest that E110 dye molecules became less ordered during the adsorption process than at equilibrium, with greater freedom on the surface of the chemically treated grape leaves.
