Ambiguous Identification Research Articles

A machine learning-based approach for mapping leachate contamination using geoelectrical methods

Leachate is the main source of pollution in landfills and its negative impacts continue for several years even after landfill closure. In recent years, geophysical methods are recognized as effective tools for providing an imaging of the leachate plume. However, they produce subsurface cross-sections in terms of individual physical quantities, leaving room for ambiguities on interpretation of geophysical models and uncertainties in the definition of contaminated zones. In this work, we propose a machine learning-based approach for mapping leachate contamination through an effective integration of geoelectrical tomographic data. We apply the proposed approach for the characterization of two urban landfills. For both cases, we perform a multivariate analysis on datasets consisting of electrical resistivity, chargeability and normalized chargeability (chargeability-to-resistivity ratio) data extracted from previously inverted model sections. By executing a K-Means cluster analysis, we find that the best partition of the two datasets contains ten and eleven classes, respectively. From such classes and also introducing a distance-based colour code, we get updated cross-sections and provide an easy and less ambiguous identification of the leachate accumulation zones. The latter turn out to be characterized by coordinate values of cluster centroids<3 Ωm and >27 mV/V and 11 mS/m. Our findings, also supported by borehole data for one of the investigation sites, show that the combined use of geophysical imaging and unsupervised machine learning is promising and can yield new perspectives for the characterization of leachate distribution and pollution assessment in landfills.

Petera (Piotra) Lachmanna życie w przestrzeni przekładu

Peter (Peter) Lachmann’s Life in the Space of Translation The article discusses Przemysław Chojnowski’s research on the work of Peter (Piotr) Lachmann, a German-Polish author born in pre-war Gliwice (Gleiwitz). Lachmann consistently thematizes the experience of the borderland, describing his existence between two languages and cultures, that is, a “life in translation”. The culmination of Chojecki’s long-time research is the monograph Liminalność i bycie „pomiędzy” w twórczości Petera (Piotra) Lachmanna. Studium literacko-kulturowe (Kraków, 2020), which is the main subject of analysis in this article. This work situates itself in the domain of modern anthropological-cultural comparative studies, clearly and convincingly demonstrating the heuristic potential of such approach. Chojnowski’s book is not only a mine of information about the work of Peter (Piotr) Lachmann, but also an invitation to discuss the broader issues of borderland and cultural bivalence, usually associated with ambiguous national identification.

MeerKAT follow-up of enigmatic GLEAM 4-Jy (G4Jy) sources

ABSTRACT We present the results from studying 140 radio sources in the GLEAM (GaLactic and Extragalactic All-sky MWA [Murchison Widefield Array]) 4-Jy (G4Jy) Sample. These sources were followed-up with MeerKAT to assess their radio morphology and enable host-galaxy identification, as existing radio images of 25 to 45-arcsec resolution do not provide sufficient information. We refer to these sources as the MeerKAT-2019 subset. The aim is to identify the host galaxy of these sources by visually inspecting the overlays comprising radio data from four surveys (at 150, 200, 843/1400, and 1300 MHz). Our morphological classification and host-galaxy identification relies upon the ∼7-arcsec resolution images from MeerKAT (1300 MHz). Through the visual inspection of the overlays, 14 radio sources in the MeerKAT-2019 subset have wide-angle tail morphology, 10 are head-tail, and 5 have X-, S-/Z-shaped morphology. Most of the remaining sources have the radio morphology of typical symmetric lobes. Of 140 sources, we find host galaxies for 98 sources, leaving 42 with no identified host galaxy. These 42 sources still have ambiguous identification even with higher resolution images from MeerKAT.

Misclassification rate of the angiographically identified culprit lesion in NSTEMI

Abstract Background Correct identification of the culprit lesion in NSTEMI is essential, in particular for patients in whom a culprit-only strategy is attractive (e.g., elderly and frail patients). However, when identifying the culprit lesion in NSTEMI, angiography can be ambiguous and correct culprit identification can therefore be challenging when based on angiography, ECG- and echocardiographic changes alone (standard-of-care). In fact, this challenge remains unresolved and is a continuous limitation in guidelines and in the few clinical trials investigating the revascularization strategy in NSTEMI. Purpose We aimed to investigate the agreement between angiography and cardiac magnetic resonance (CMR) and optical coherence tomography (OCT) in identifying the culprit lesion in non-ST segment elevation myocardial infarction (NSTEMI). Methods In two centres we prospectively enrolled 104 patients. CMR was performed prior to angiography. Operators, blinded to CMR, identified a culprit lesion based on angiography and standard-of-care. OCT was subsequently performed on operator-suspected culprit lesions and stenoses ≥50% diameter. CMR and OCT were reviewed blinded to angiographic culprit identification. Myocardial oedema on CMR was considered the reference standard for a culprit. In the absence of oedema, OCT was used. In case of multiple suspected OCT-lesions, hierarchical criteria for culprit identification were used: acute thrombus &amp;gt; plaque rupture with a cavity &amp;gt; organising thrombus &amp;gt; dissection &amp;gt; calcific nodule. Results The majority of included patients were male (75%) at a mean 63 years of age. Obstructive disease was observed in 85 (82%) patients, of which 53 (51%) had multivessel disease. On a patient-level, angiography identified a culprit lesion in 90 (87%) patients, of which CMR/OCT only identified a culprit in 74 (82%) patients. This constituted a moderate overall positive predictive value of angiography, which was found inferior to CMR/OCT in identifying the culprit lesion. On a lesion-level, CMR/OCT identified a different culprit lesion than angiography in 12 (16%) patients. Of these, only one patient did not receive revascularization of the true culprit lesion. Moreover, in the 14 patients without an angiographic culprit, CMR/OCT identified a culprit in 7 (50%) patients. Thus, angiography including standard-of-care falsely identified the culprit lesion in overall 35 (34%) patients: 7 false negatives, 16 false positive at patient-level, and 12 misclassified angiographic culprits on lesion-level (Figure 1). Specifically, OCT identified 13% misclassified culprit lesions in proximal segments, and provided an added diagnostic value. Conclusions Angiography misclassified the culprit lesion in one in three patients with NSTEMI with respect to both presence and location. OCT complemented angiography in ambiguous cases which underscores the value of OCT in aiding treatment and diagnosis in NSTEMI. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Research Grant at Rigshospitalet, Copenhagen University Hospital, DenmarkNovo Nordisk Foundation, Denmark

Automated filtering scripts based on accurate mass applied to comprehensive two-dimensional gas chromatography coupled to high resolution mass spectrometry for group-type analysis of naphthenic acids

In this work, it is reported the detailed evaluation of filtering scripts for comprehensive two-dimensional gas chromatography coupled with high-resolution mass spectrometry (GC×GC–HRMS). Automated scripts were built to perform group-type analysis of naphthenic acids (NAs) in complex mixtures, like produced water (PW) samples. The logical operations were based on high-resolution mass spectral information acquired by using electron ionization (EI) and chemical ionization in the positive mode (PCI). The most common derivatization methods were carefully evaluated for group-type analysis. The performance of the scripts was evaluated using the rank metric (RM), wherein the sum-normalized absolute difference was calculated for true positives versus false positives. The best performing approach for qualitative analysis was attained by combining the derivatization with N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA) with EI due to the high intensity and characteristic pseudo-molecular ions (even-electron [M-57]+). The data obtained supported the development of selective scripts, exhibiting accuracy values above 98 % and 88 % for the reference and unknown PW samples, respectively. The retention pattern of GC×GC was also used as a discriminant parameter for ambiguous identification, highlighting the synergy between GC×GC and HRMS. The proposed scripts are an interesting alternative to profile 154 classes of MTBSTFA-derived NAs, which considerably reduced the time spent in manual data curation and annotation, while improving the reliability of the results. This is an important step to aid in the development of standard methods for the study and regulation of water-soluble compounds found in PW samples.

Research on Judicial application of Village Regulations and Civil Covenants in Civil Disputes

With the promotion of the rural revitalization strategy and the transformation of rural society, the number of cases involving civil disputes of village regulations and civil agreements is increasing. Due to the lack of relevant judicial rules, courts are faced with judicial dilemmas such as unclear scope of accepting cases, different standards of legality review, and ambiguous identification of membership of collective economic organizations. Based on 386 judicial precedents related to village regulations and civil covenants in civil disputes, data analysis and countermeasures are carried out. By clarifying the scope of cases accepted by the court, the subject and standard of legality review, and the standard of collective economic organization membership identification, the rules provide countermeasures for the judicial organs to resolve the civil disputes of village regulations and civil agreements, so as to protect the legitimate rights and interests of villagers.

Fragrant and Beneficial – The Quest for Recreation of Fragrances from Early Islamic States

Fragrances may appear to be a frivolous topic in the modern world, but well into the early modern period they were firmly located at the intersection of medicine and luxury products. The fragrances created by physicians, pharmacists and perfumers in the early Islamic states influenced the olfactory traditions of Europe and Far East Asia. To study the fragrances of medieval China or the middle Byzantium, one needs first to understand how they were made at the source. Many if not most of fragrance recipes are hidden in medical texts, cookbooks (as art of food preparation was considered a part of medicine), and independent treatises on perfumery. Besides being used for pleasure, as aphrodisiacs, or to satisfy the desire for luxury exotica, pleasant odours were associated with good health, and therefore were believed to have a therapeutic effect. In many cases, we have the direct statement of the author that a given incense or scented powder could cure body weakness, alleviate pain, or help with confusion after a stroke (Hamerneh, 1965). The beneficial effect may have been related more to the placebo effect though it is confirmed that odours can influence the human mind. In my quest for accurate recreation of the beneficial fragrances, I study the processes used to prepare them by the original creators. This includes preparation of compound ingredients like ramik and sukk or distilled fragrant waters. I cross-reference different sources to identify ingredients although the ambiguity of identification cannot be fully eliminated. I attempt to source my ingredients, pending financial and legal limitations, from the same geographical locations referenced in the early medieval period. When the original ingredient is no longer available due to overharvesting or changes in plant biology, I look for a suitable replacement in contemporary sources, as many authors included chapters on substitutions (Levey, 1971). As an independent scholar, my ability to study the effects of these recreated fragrances is limited to the assessment by olfactory approach (like in the experimental archaeology of food). I observe the stability and maturation of fragrances over time, as well as their longevity when applied to skin. A group of my volunteer testers helps with this task. They report if they liked the fragrance undergoing assessment, and how they felt about the odour. I am often surprised how the view of the same scent can range from ‘absolute stench’ to ‘good mood fragrance.’ I also organize presentations of the fragrances to introduce the public to the forgotten scents. I believe it is our duty to celebrate the traditions of all cultures, and this includes the amazing fragrances from the early caliphates, from Al-Andalus to the Near East and beyond.

Sample-multiplexing approaches for single-cell sequencing.

Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.

An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics

BackgroundProteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases, which only contain proteins whose transcripts are detected in the sample-matched transcriptome. These were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach.ResultsWe establish that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative false discovery rates (FDRs) are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible.ConclusionsIn summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications.

Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex predicted by deep learning of experimental recordings.

Biologically realistic computer simulations of neuronal circuits require systematic data-driven modeling of neuron type-specific synaptic activity. However, limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal identification have so far prevented the consistent characterization of synaptic signals for all connections of any neural system. We introduce a strategy to overcome these challenges and report a comprehensive synaptic quantification among all known neuron types of the hippocampal-entorhinal network. First, we reconstructed >2600 synaptic traces from ∼1200 publications into a unified computational representation of synaptic dynamics. We then trained a deep learning architecture with the resulting parameters, each annotated with detailed metadata such as recording method, solutions, and temperature. The model learned to predict the synaptic properties of all 3,120 circuit connections in arbitrary conditions with accuracy approaching the intrinsic experimental variability. Analysis of data normalized and completed with the deep learning model revealed that synaptic signals are controlled by few latent variables associated with specific molecular markers and interrelating conductance, decay time constant, and short-term plasticity. We freely release the tools and full dataset of unitary synaptic values in 32 covariate settings. Normalized synaptic data can be used in brain simulations, and to predict and test experimental hypothesis.

Substantial Improvement in Nontuberculous Mycobacterial Identification Using ASTA MicroIDSys Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry with an Upgraded Database.

Identifying Mycobacterium using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is challenging. We evaluated the performance of MALDI-TOF MS in identifying nontuberculous mycobacteria (NTM) using the ASTA MicroIDSys system (ASTA Inc., Suwon, Korea) with the MycoDB v1.95s and upgraded MycoDB v2.0-beta databases. We tested 124 NTM isolates collected from Ogawa medium at Severance Hospital, Seoul, Korea, between January and April 2019. MicroIDSys scores were categorized into three groups: ≥140, reliable identification; 130–139, ambiguous identification; and <130, invalid identification. To validate the results, we used the reverse blot hybridization assay (Molecutech REBA MycoID, YD Diagnostics Corp., Korea). Initial analysis using MycoDB v1.95s resulted in 26.6% (33/124) reliable, 43.5% (54/124) ambiguous, and 29.8% (37/124) invalid identifications. Re-analysis using the upgraded MycoDB v2.0-beta database resulted in 94.4% (117/124) reliable, 4.0% (5/124) ambiguous, and 1.6% invalid (2/124) identifications. The percentage of reliable identifications that matched with the reference increased from 26.6% (33/124) with MycoDB v1.95s to 93.5% (116/124) with MycoDB v2.0-beta. The upgraded databases enable substantially improved NTM identification through deep learning in the inference algorithm and by considering more axes in the correlation analysis. MALDI-TOF MS using the upgraded database unambiguously identified most NTM species. Our study lays a foundation for applying MALDI-TOF MS for the simple and rapid identification of NTM isolated from solid media.

Reproducing privilege through whiteness and beauty: an intersectional analysis of elite Chilean university students’ practices

This paper undertakes an intersectional analysis of the ways in which socio-economically elite higher education students in Chile reproduce privilege through everyday practices of whiteness and beauty. Drawing on qualitative data from interviews and observations with 20 privileged students at an elite Chilean university, the paper identifies and discusses the students’ ambiguous racial identifications and the racialized, classed and gendered ways in which privilege was enacted through practices of ‘beauty’ as a ‘natural’ embodied distinction. We discuss how ideals and practices of beauty were a ‘double edged sword’ for elite female students, conferring advantage while also denying academic authenticity. We argue that Chilean elite performances of whiteness offer an added layer of complexity to existing literature, problematising and decentring performances of whiteness from white bodies. We conclude by suggesting that an intersectional lens offers a useful way to understand and address the re/production of inequalities in Chilean elite universities.

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications.

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

Diversity investigation by application of DNA barcoding: A case study of lepidopteran insects in Xinjiang wild fruit forests, China.

To investigate the species diversity of lepidopteran insects in Xinjiang wild fruit forests, establish insect community monitoring systems, and determine the local species pool, we test the applicability of DNA barcoding based on cytochrome c oxidase subunit I (COI) gene for accurate and rapid identification of insect species. From 2017 to 2019, a total of 212 samples with ambiguous morphological identification were selected for DNA barcoding analysis. Five sequence‐based methods for species delimitation (ABGD, BINs, GMYC, jMOTU, and bPTP) were conducted for comparison to traditional morphology‐based identification. In total, 2,422 samples were recorded, representing 143 species of 110 genera in 17 families in Lepidoptera. The diversity analysis showed that the richness indices for Noctuidae was the highest (54 species), and for Pterophoridae, Cossidae, Limacodidae, Lasiocampidae, Pieridae, and Lycaenidae were the lowest (all with 1 species). The Shannon–Wiener species diversity index (H′) and Pielou's evenness (J′) of lepidopteran insects first increased and then decreased across these 3 years, while the Simpson diversity index showed a trend of subtracted then added. For molecular‐based identification, 67 lepidopteran species within 61 genera in 14 families were identified through DNA barcoding. Neighbor‐joining (NJ) analysis showed that conspecific individuals were clustered together and formed monophyletic groups with a high support value, except for Lacanobia contigua (Denis & Schiffermüller, 1775) (Noctuidae: Hadeninae). Sixty‐seven morphospecies were classified into various numbers of MOTUs based on ABGD, BINs, GMYC, jMOTU, and bPTP (70, 96, 2, 71, and 71, respectively). In Xinjiang wild fruit forests, the family with the largest number of species is Noctuidae, followed by Geometridae, Crambidae, and the remaining families. The highest Shannon diversity index is observed for the family Noctuidae. Our results indicate that the distance‐based methods (ABGD and jMOTU) and character‐based method (bPTP) outperform GMYC. BINs is inclined to overestimate species diversity compared to other methods.

Common potato disease symptoms: ambiguity of symptom-based identification of causal pathogens and value of on-site molecular diagnostics

Common potato disease symptoms: ambiguity of symptom-based identification of causal pathogens and value of on-site molecular diagnostics

A framework to standardize causes of construction delay

Available literature shows no standard method or framework to identify critical causes of delay and assess its impact on the construction project. The use of ambiguous phrases and unstructured identification of causes has resulted in the ineffectiveness of the study findings. Therefore, this study has developed a framework to identify critical causes of delay through a systematic approach. This systematic approach helps to identify critical causes of delay in a standardized format. The framework categorically sequences causes of delay and connects them to the responsible party. From the obtained data, three hundred thirty-five critical causes of delay were extracted, categorized into 14 groups, and summarized into 48 major critical causes presented in tables. This study provides a comprehensive list and reference of the critical causes identified in the past 10 years. The study also quantitatively analysed the collected data presented in tables. The developed framework and approach minimize alternate and ambiguous phrasing, enhance study findings application in practice, and increases the research outcome’s consistency.

GC-MS/MS Profiling of Plant Metabolites.

Gas chromatography coupled to electron ionization (EI) quadrupole mass spectrometry (GC-MS) is currently one of the most developed and robust metabolomics technologies. This approach allows for simultaneous measurements of large number of chemically diverse compounds including organic acids, amino acids, sugars, sugar alcohols, aromatic amines, and fatty acids. Untargeted GC-MS profiling based on full scan data acquisition requires complicated raw data processing and sometime provides ambiguous metabolite identifications. Targeted analysis using GC-MS/MS can provide better specificity, increase sensitivity, and simplify data processing and compound identification but wider application of targeted GC-MS/MS approach in metabolomics is hampered by the lack of extensive databases of MRM transitions for non-derivatized and derivatized endogenous metabolites. The focus of this chapter is the automation of GC-MS/MS method development which makes it feasible to develop quantitative methods for several hundred metabolites and use this strategy for plant metabolomics applications.

DeepLC can predict retention times for peptides that carry as-yet unseen modifications.

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

Analyzing Assay Specificity in Metabolomics Using Unique Ion Signature Simulations.

Targeted, untargeted, and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have been popularized in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex coisolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite detection in complex backgrounds using precursor and fragment ion mass-to-charge (m/z) separation, comparing three benchmarked mass spectrometry (MS) methods [MS1, MRM (multiple reaction monitoring), and DIA]. Our simulations show that DIA outperformed MS1-only and MRM-based methods with regards to specificity by factors of ∼2.8-fold and ∼1.8-fold, respectively. Additionally, we show that our results are not dependent on the number of transitions used or the complexity of the background matrix. Finally, we show that collision energy is an important factor in unambiguous detection and that a single collision energy setting per compound cannot achieve optimal pairwise differentiation of compounds. Our analysis demonstrates the power of using both high-resolution precursor and high-resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both data-dependent acquisition (DDA) and MRM with regards to unique compound identification potential.

Anatomical characterization of 18 commercially important varieties of Phoenix dactylifera L. by using microscopy

Date palm (Phoenix dactylifera L.) is an important traditional crop having socioeconomic values. To best of literature review and our knowledge, there is a gap of taxonomic study on the role of morpho-anatomical features of date palm for correct identification of its varieties. With the advancement in the field of microscopy, anatomical features are considered very effective taxonomical markers. The present study was aimed to analyze the taxonomic variations using leaf epidermal anatomy and to highlight the importance of leaf epidermis to resolve identification ambiguities among the different varieties of date palms. The study includes analysis of leaf epidermal anatomy of 18 different commercial varieties of date palm collected from different areas of Pakistan. A diverse range of shape and size of epidermal cells, subsidiary cells, guard cells, stomata, stomatal pore was observed on abaxial as well as adaxial among studied date palm varieties via LM and found to be very helpful tool in discrimination of varieties. The shape of epidermal cells on both abaxial and adaxial surfaces varies including elongated, irregular, polygonal, curved, and spherical or sometime mixed of these. The values of stomatal indices on both surfaces showed wide variations.