Uncertain Identification Research Articles

230 Using phenotypes with uncertain identification leveraging computer vision models

Abstract There has been an increase in high throughput phenotyping using sensors in livestock farming. The major obstacle when analyzing phenotypic data in studies using cameras is identifying the animals using computer vision. To overcome this problem, an analysis method that uses keypoint detection in conjunction with classification algorithms can identify animals in images. However, these algorithms generally output a prediction probability for different classes, reflecting uncertainty. In most practices, the highest probability will be assigned as the putative identification (ID). In some cases, this may introduce incorrect identification and affect downstream decisions. Here, we propose an analytical approach to incorporate all the uncertainty in the putative ID distribution into downstream inferences. We used a publicly available keypoint detection dataset of 17 keypoints detected in 3,226 images from 30 diverse thoroughbred horses. We performed feature engineering with Linear Discriminant Analysis (LDA) using 5-fold cross-validation to build horse classifiers. The best model selected had an accuracy of 88.12% to identify animals in the testing set. Afterward, we performed Monte Carlo simulations to add a phenotype that was assumed independent of the image keypoints. This generated a set of plasmodes to evaluate methods that accounted for ID uncertainty. Each simulation included different means for two groups of 15 horses. In total nine simulation scenarios were considered including varying effect sizes of group mean difference and various repeatability values (horse variance to total variance ratio). We analyzed the simulated data with three different mixed models: 1) Fixed and random effects were based on using ground truth assignment of ID, 2) Fixed and random effects were based on using the putative ID with the highest posterior probability from the best classifier, 3) Finally, prediction probabilities were used to account for putative ID uncertainty. As expected, the model that used ground truth ID had the highest power to detect group differences, and it had the lowest root mean square error (rMSE) to estimate variance components across all scenarios. The linear model that used highest posterior probability putative ID, showed the lowest power and highest rMSE. For instance, as shown in Table 1, the mean rMSE value for group mean differences in the model using ground truth is 0.01 and the model using highest posterior probabilities is 0.13. The model that incorporated ID uncertainty had intermediate properties, showing a rMSE of 0.03 for group differences. Overall, incorporating uncertainty in Animal ID results in improved performance in ultimate test statistics used in animal studies. This is very important in breeding and precision livestock farming contexts where animal identification is challenging. We will further investigate this.

ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data.

The taxonomic assignment of sequences obtained by high throughput amplicon sequencing poses a limitation for various applications in the biomedical, environmental, and agricultural fields. Identifications are constrained by the length of the obtained sequences and the computational processes employed to efficiently assign taxonomy. Arriving at a consensus is often preferable to uncertain identification for ecological purposes. To address this issue, a new tool called "ASVmaker" has been developed to facilitate the creation of custom databases, thereby enhancing the precision of specific identifications. ASVmaker is specifically designed to generate reference databases for allocating amplicon sequencing data. It uses publicly available reference data and generates specific sequences derived from the primers used to create amplicon sequencing libraries. This versatile tool can complete taxonomic assignments performed with pre-trained classifiers from the SILVA and UNITE databases. Moreover, it enables the generation of comprehensive reference databases for specific genes in cases where no directly applicable database exists for taxonomic classification tools.

High Species Richness of Decapod Crustaceans on an Urban Rocky Shore Beach

The rocky intertidal shore of La Caleta, an urban beach located in Cádiz (SW Spain), was surveyed for decapod crustaceans over a year. Samplings were taken monthly (March 2015 to February 2016) in three differentiated zones during the spring low tides of every month at five sites, differentiated according to the hydrodynamic regimes and intertidal levels. A qualitative sampling (present/absent) was carried out by visual identification (minimally invasive), and only those specimens with uncertain identification were collected by hand, studied at the laboratory, or identified using their DNA barcode. A total of 44 species were identified. Comparison with species richness of decapod crustaceans in other Atlantic and Mediterranean intertidal or subtidal ecosystems (including protected areas) shows higher values in La Caleta, a surprising fact considering the type of habitat and its placement (an urban and unprotected shore).

Single-Molecule Real-Time Sequencing to Explore the Mycobiome Diversity in Malt.

ABSTRACTThis study determined the composition of fungal communities and characterized the enriched fungal species in raw and roasted malts via the third-generation PacBio-based full-length single-molecule real-time (SMRT) sequencing of the full-length amplicon of the internal transcribed spacer (ITS) region. In total, one kingdom, six phyla, 23 classes, 56 orders, 120 families, 188 genera, 333 species, and 780 operational taxonomic units (OTUs) were detected with satisfactory sequencing depth and sample size. Wickerhamomyces (56%), Cyberlindnera (15%), Dipodascus (12%), and Candida (6.1%) were characterized as the dominant genera in the raw malts, and Aspergillus (35%), Dipodascus (21%), Wickerhamomyces (11%), and Candida (3.5%) in the roasted malts. Aspergillus proliferans, Aspergillus penicillioides, and Wickerhamomyces anomalus represented the crucial biomarkers causing intergroup differences. Correlation analysis regarding environmental factors indicated that the water activity (aw) of the samples affected the composition of the fungal communities in the malts. In practice, special attention should be paid to the mycotoxin-producing fungi, as well as other fungal genera that are inversely correlated with their growth, to ensure the safe use of malt and its end products.IMPORTANCE Fungal contamination and secondary metabolite accumulation in agricultural products represent a global food safety challenge. Although high-throughput sequencing (HTS) is beneficial for explaining fungal communities, it presents disadvantages, such as short reads, species-level resolution, and uncertain identification. This work represents the first attempt to characterize the fungal community diversity, with a particular focus on mycotoxin-producing fungi, in malt via the third-generation PacBio-based full-length SMRT sequencing of the ITS region, aiming to explore and compare the differences between the fungal communities of raw and roasted malts. The research is beneficial for developing effective biological control and conservation measures, including improving the roasting conditions, monitoring the environmental humidity and aw, and effectively eliminating and degrading fungi in the industry chain according to the diverse fungal communities determined, for the safe use of malts and their end products, such as beers. In addition, the third-generation SMRT sequencing technology allows highly efficient analysis of fungal community diversity in complex matrices, yielding fast, high-resolution long reads at the species level. It can be extended to different research fields, updating modern molecular methodology and bioinformatics databases.

Mediterranean spreading of the bicolor purse oyster, Isognomon bicolor, and the chicken trigger, Malleus sp., vs. the Lessepsian prejudice

The introduction rate of alien species in the Mediterranean Sea is rapidly growing, and their taxonomical identification is increasingly challenging. This uncertain identification often leads to an incorrect estimation of the number of alien species, their route of introduction, and their potential negative effects. This is particularly true for some bivalves, which are characterized by high variation in their shells, resulting in uncertain morphological identification. This is the case for two alien bivalves, i.e., an Isognomonidae and a Malleidae species, both characterized by confused historical colonization records in the Mediterranean Sea, misidentifications, and controversial and changing nomenclatures that have insofar negatively affected our knowledge on their geographical distributions. In this respect, molecular approaches provide a strategy that is especially useful when traditional taxonomy fails, and DNA barcoding is a powerful and well-known tool to obtain reliable identifications through efficient molecular markers. In this work, we used the 16S rRNA marker to assess the preliminary identification of Isognomon sp. and Malleus sp. specimens from different localities in the Southern Mediterranean Sea. Bayesian inference (BI) and maximum likelihood (ML) methods were applied to test the monophyly of the phylogenetic linages and to clarify their taxonomic positions, allowing a complete overview of the colonization and spreading of these two alien bivalves in the Mediterranean Sea. In particular, the Isognomon sp. specimens were identified as the Atlantic I. bicolor, highlighting that previously suggested invasive migration patterns, (i.e., the Lessepsian migration), must be reconsidered with stronger critical attention in light of currently occurring global changes.

New protosirenid (Mammalia, Sirenia) in the late Eocene sea cow assemblage of southwestern Morocco

Two sirenian species are present in the late Eocene Samlat Formation near Ad-Dakhla in southwestern Morocco. A well preserved mandible with left and right dentaries belongs to a new protosirenid genus and species Dakhlasiren marocensis closely related to the genus Protosiren. An early dugongid of uncertain identification (cf. Eotheroides sp.) is also present, represented by vertebrae and ribs. Protosirenids differ from dugongids in the form of the brain, size and separation of nasal bones, and conformation of the anterior mandible. Protosirenids also differ in having vertebrae with larger neural canals, in having ligamentous rib articulations, and in lacking the pachyostotic ribs characteristic of dugongids. We tentatively interpret the latter differences to be related to feeding on softer vegetation farther offshore, with a thoracic rete mirabile for counter-current heat exchange and a collapsible rib cage to enable deeper dives. Dakhlasiren seemingly carried the divergent specializations of Protosiren a step farther by reduction of tongue musculature and loss of a masticatory surface at the front of the mandible.

Multi‐observer methods for estimating uncertain species identification

AbstractSpecies identification typically is assumed certain in population ecology. However, partial identification (to lowest certain taxon) and misidentification (collectively “uncertain identification”) have pervaded surveys. Consequent bias in occupancy and abundance estimates impairs inference and decision‐making, but methods for estimating uncertain identification and remedying its effects have required restrictive assumptions or utilized artificial cues. I present likelihood‐based multi‐observer methods for estimating uncertain identification during surveys enumerating individuals, assess performance via simulation study, demonstrate application to line transects, and provide recommendations for use and further development. Methods typically employ three or more independent observers using one or more observation methods. Species identity is a latent state, and discrepancies in species classifications of the same individuals or groups enable estimation of uncertain identification and true species composition of detected individuals. Because complete or certain classifications are not required for any observer, methods uniquely befit surveys for free‐ranging, unmarked animals. Methods permit arbitrary numbers of species and observation states, multi‐species groups, covariates, and distinct uncertain identification for each species, observer, and observation state. Innovative technologies for acquiring recorded observations (e.g., digital media of survey observations) complement methods by enabling post hoc classifications by independent observers and enhanced species identification (e.g., replays, magnification, spectrograms). Using multi‐observation methods (MOM), classifications by observers at sampling sites are supplemented with classifications from recorded observations by additional observers. Alternatively, recorded observations can be acquired solely via technology (e.g., unmanned aerial systems, autonomous recording units). Multi‐observation methods enable supplementing automated classifications (e.g., machine learning) with classifications by humans, while single‐observation methods allow classifications by multiple human observers. Simulations demonstrated good model performance and robustness, except to high un‐modeled heterogeneity in misidentification. Minimizing misidentification and other practical steps enhance precision and accuracy of results. I applied MOM to line transects for two genusBrachyramphusmurrelet species by photographing a sample of detected groups. Post hoc classifications from photographs by three independent observers had lower misidentification and partial identification (0.6%, 9%) vs. experienced observers at survey sites (3.2%, 23%). Integrating multi‐observer methods with other methods (e.g., distance sampling,N‐mixture models) enables estimation of occupancy or abundance accounting for uncertain detection and identification.

Identification and Characterization of 33 Bacillus cereus sensu lato Isolates from Agricultural Fields from Eleven Widely Distributed Countries by Whole Genome Sequencing

The phylogeny, identification, and characterization of 33 B. cereus sensu lato isolates originating from 17 agricultural soils from 11 countries were analyzed on the basis of whole genome sequencing. Phylogenetic analyses revealed all isolates are divided into six groups, which follows the generally accepted phylogenetic division of B. cereus sensu lato isolates. Four different identification methods resulted in a variation in the identity of the isolates, as none of the isolates were identified as the same species by all four methods—only the recent identification method proposed directly reflected the phylogeny of the isolates. This points to the importance of describing the basis and method used for the identification. The presence and percent identity of the protein product of 19 genes potentially involved in pathogenicity divided the 33 isolates into groups corresponding to phylogenetic division of the isolates. This suggests that different pathotypes exist and that it is possible to differentiate between them by comparing the percent identity of proteins potentially involved in pathogenicity. This also reveals that a basic link between phylogeny and pathogenicity is likely to exist. The geographical distribution of the isolates is not random: they are distributed in relation to their division into the six phylogenetic groups, which again relates to different ecotypes with different temperature growth ranges. This means that we find it easier to analyze and understand the results obtained from the 33 B. cereus sensu lato isolates in a phylogenetic, patho-type and ecotype-oriented context, than in a context based on uncertain identification at the species level.

Uncertain identification method of structural damage for beam-like structures based on strain modes with noises

Strain modal parameters are more sensitive to the local structural damage than displacement modal parameters. Therefore, it will be more effective to locate damage based on the change of strain modes. In this paper, the sensitivity matrix between strain mode and elemental stiffness parameter is deduced. Further more, the relationship of strain modal information and damage parameter of beam-like structures is established. In order to quantify the influence of the uncertainty in the strain modal information on the identification results, this paper treats measurement noise as probabilistic and interval variable and deduces the corresponding formula of structural elemental stiffness parameters. Due to the definition of uncertainties, different damage indexes including deterministic index, uncertainty index and combination index are established or introduced to quantify the damage extent and damage possibility. The validity of the proposed method is verified by a numerical example and an experimental work. The effectiveness and robustness of the displacement modal information based method is compared with the proposed method. The characteristics of different damage indexes are discussed by statistical means in the numerical example. The results indicates that the combination index has more advantages in identifying the damage under uncertainties.

The true nature of Ganoderma in Iran: Taxonomy based on ITS and mtSSU rDNA

AbstractThe genus Ganoderma Karst. has broad‐spectrum usage in biotechnology, medicine and the food industry. The complexity of the morphology within species has led to uncertain identification in the past, but recent advancements in molecular identification methods have provided scientists with the opportunity to better understand the taxonomy of the species. The present study attempts for the first time to elucidate the distinctiveness of the Ganoderma species growing in Iran concerning those elsewhere in the world based on mitochondrial small subunit ribosomal DNA (mtSSU rDNA) and internal transcribed spacer (ITS) sequence data. The results disclosed that the G. lucidum Karst. samples collected in Iran are more similar to the European Ganoderma species than to the Asian (Chinese) one used in this study.

The GLEAM 4-Jy (G4Jy) Sample: II. Host galaxy identification for individual sources

Abstract The entire southern sky (Declination, $\delta< 30^{\circ}$ ) has been observed using the Murchison Widefield Array (MWA), which provides radio imaging of $\sim$ 2 arcmin resolution at low frequencies (72–231 MHz). This is the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we have previously used a combination of visual inspection, cross-checks against the literature, and internal matching to identify the ‘brightest’ radio-sources ( $S_{\mathrm{151\,MHz}}>4$ Jy) in the extragalactic catalogue (Galactic latitude, $|b| >10^{\circ}$ ). We refer to these 1 863 sources as the GLEAM 4-Jy (G4Jy) Sample, and use radio images (of ${\leq}45$ arcsec resolution), and multi-wavelength information, to assess their morphology and identify the galaxy that is hosting the radio emission (where appropriate). Details of how to access all of the overlays used for this work are available at https://github.com/svw26/G4Jy. Alongside this we conduct further checks against the literature, which we document here for individual sources. Whilst the vast majority of the G4Jy Sample are active galactic nuclei with powerful radio-jets, we highlight that it also contains a nebula, two nearby, star-forming galaxies, a cluster relic, and a cluster halo. There are also three extended sources for which we are unable to infer the mechanism that gives rise to the low-frequency emission. In the G4Jy catalogue we provide mid-infrared identifications for 86% of the sources, and flag the remainder as: having an uncertain identification (129 sources), having a faint/uncharacterised mid-infrared host (126 sources), or it being inappropriate to specify a host (2 sources). For the subset of 129 sources, there is ambiguity concerning candidate host-galaxies, and this includes four sources (B0424–728, B0703–451, 3C 198, and 3C 403.1) where we question the existing identification.

A stair-step probabilistic approach for automatic anomaly detection in building ventilation system operation

HVAC systems contribute to a large part of energy consumption in buildings and studies suggest that savings up to 30% can be achieved by utilising the potential of FDD methods which aim to identify faults and their root causes. In particular, model-based FDD are becoming more useful as the modelling and simulation of complex building systems have been eased due to advancements within the field. However, methods often lack the ability of effectively distinguishing between healthy and abnormal operation and some are highly subject to human evaluation. Bang et al. proposed a model-based fault detection method for automatic identification of abnormal energy performance on a daily basis in building ventilation units using a statistical definition of abnormality based on the Chernoff bound. The method enables the fault detection process to be automated which removes the need for human evaluation. However, the method is governed by linear interpolation leading to uncertain identification of abnormal operation and imprecise probability calculations, thereby triggering the need for modifications. This work upgrades the model-based fault detection method by introducing a stair-step approach to more accurately identify abnormal behaviour. The outcomes of the upgraded approach are reported for a case study building and evaluated in comparison with the original method. The improved method shows correct identification of abnormal periods and detected the precise day of a faulty occupancy counter. Moreover, it shows that the ascribed probabilities of the original approach are consequently lower for the two analysed ventilation units by an average of 13 and 15% points, respectively.

A multi-objective optimization strategy to reduce correlation and uncertainty for thermal response test analysis

The analysis of thermal response tests by optimization-based methods can lead to highly correlated parameters and to a relatively uncertain identification of the thermal parameters. The multi-objective optimization strategy examined here combines the common root mean square error to describe the misfit between the measured and simulated fluid temperature with the root mean square error between the temperature derivative of the same variables. The final objective function combines also the partial correlation coefficients of the temperature and derivative signals. The four objective functions are combined by scalarizing the multi-objective problem. The method proposed here was tested using three real data sets produced by the thermal response tests performed on a helical shaped pipe and on two single U-bent ground heat exchangers. The multi-objective strategy did not lead to different thermal parameters for the three tests, but it did generate a significant reduction in the correlation and uncertainty of the parameters. In addition, a more compact optimum shape can lead to a more rapid convergence time.

The Complexity of the Identification of Burkholderia cepacia Strain Which Caused Septicemia

Background: The correct identifying of pathogenic Burkholderia spp. using available commercial biochemical systems is a certain problem due to metabolic plasticity and variable enzymatic profile of isolates. Objectives: The current study aimed at using specific PCR and conventional multi-locus sequence typing (MLST) scheme to confirm the uncertain identification results for Burkholderia cepacia clinical isolate. Methods: Multilocus species-specific PCR and MLST profiling of high-throughput sequencing data have been used to clarify the varied results of biochemical identification of the strain. Results: The strain isolated from a patient with septicemia was initially identified as B. pseudomallei by Vitek 2 GN system but has an uncharacteristic antimicrobial resistance pattern and colony morphology. A species-specific multilocus PCR and whole-genome sequence profiling, according to the MLST scheme, allowed to identify an isolate as B. cepacia. Conclusions: The obtained results demonstrate the preference of molecular tests for correct identifying of pathogenic Burkholderia, considering that misidentification of those may lead to improper treatment or increase of biosafety risk.

IDrone

Swarms of Unmanned Aerial Vehicles (drones) could provide great benefit when used for disaster response and indoor search and rescue scenarios. In these harsh environments where GPS availability cannot be ensured, prior work often relies on cameras for control and localization. This creates the challenge of identifying each drone, i.e., finding the association between each physical ID (such as their radio address) and their visual ID (such as an object tracker output). To address this problem, prior work relies on visual cues such as LEDs or colored markers to provide unique information for identification. However, these methods often increase deployment difficulty, are sensitive to environmental changes, not robust to distance and might require hardware changes. In this paper, we present IDrone, a robust physical drone identification system through motion matching and actuation feedback. The intuition is to (1) identify each drone by matching the motion detected through their inertial sensors and from an external camera, and (2) control the drones so they move in unique patterns that allow for fast identification, while minimizing the risk of collision involved in controlling drones with uncertain identification. To validate our approach, we conduct both simulation and real experimentation with autonomous drones for the simplified case of a stationary Spotter (powerful drone equipped with the camera). Overall, our initial results show that our approach offers a great tradeoff between fast identification and small collision probability. In particular, IDrone achieves faster identification time than safety-based baseline actuation (one-at-a-time), and significantly higher survival rate compared to fast, non-safety-based baseline actuation (random motion).

Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

Abstract. Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslide-triggering thresholds. In this paper, we perform a quantitative analysis of the impacts of uncertain knowledge of landslide initiation instants on the assessment of rainfall intensity–duration landslide early warning thresholds. The analysis is based on a synthetic database of rainfall and landslide information, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model, and is therefore error-free in terms of knowledge of triggering instants. This dataset is then perturbed according to hypothetical reporting scenarios that allow simulation of possible errors in landslide-triggering instants as retrieved from historical archives. The impact of these errors is analysed jointly using different criteria to single out rainfall events from a continuous series and two typical temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant, especially when errors exceed 1 day or the actual instants follow the erroneous ones. Errors generally lead to underestimated thresholds, i.e. lower than those that would be obtained from an error-free dataset. Potentially, the amount of the underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall limits the possibility to set up links between thresholds and physio-geographical factors.

Parameter identification for structural dynamics based on interval analysis algorithm

A parameter identification method using interval analysis algorithm for structural dynamics is presented in this paper. The proposed uncertain identification method is investigated by using central difference method and ARMA system. With the help of the fixed memory least square method and matrix inverse lemma, a set-membership identification technology is applied to obtain the best estimation of the identified parameters in a tight and accurate region. To overcome the lack of insufficient statistical description of the uncertain parameters, this paper treats uncertainties as non-probabilistic intervals. As long as we know the bounds of uncertainties, this algorithm can obtain not only the center estimations of parameters, but also the bounds of errors. To improve the efficiency of the proposed method, a time-saving algorithm is presented by recursive formula. At last, to verify the accuracy of the proposed method, two numerical examples are applied and evaluated by three identification criteria respectively.

Unified uncertainty representation and quantification based on insufficient input data

Uncertainty quantification accuracy of system performance has an important influence on the results of reliability-based design optimization (RBDO). A new uncertain identification and quantification methodology is proposed considering the strong statistical variables, sparse variables, and interval variables simultaneously. Maximum likelihood function and Akaike information criterion (AIC) methods are used to identify the best-fitted distribution types and distribution parameters of sparse variables. The interval variables are represented with evidence theory. Finally, a unified uncertainty quantification framework considering the three types of uncertain design variables is put forward, and then the failure probability of system performance is quantified with belief and plausibility measures. The Kriging metamodel and random sampling method are used to reduce the computational complexity. Three examples are illustrated to verify the effectiveness of the proposed methodology.

Species delimitation in the recalcitrant moss genus Rhynchostegiella (Brachytheciaceae)

AbstractBryophytes, with their reduced morphologies and challenging taxonomy, appear as ideal candidates for the application of the fast‐developing tools of molecular species delimitation. Here, we apply species delimitation techniques to the moss genus Rhynchostegiella, which has long served as a convenient repository for small pleurocarpous species. Species delimitation analyses, including the Generalized Mixed Yule Coalescent approach and its Bayesian variant, congruently identified 13 putative species within Rhynchostegiella. To avoid inflation in the number of species with sympatric distributions that can only be recognized molecularly, we identified only one species when two or more molecular species were monophyletic, sympatric, and morphologically impossible to tell apart. After exclusion of 33 species from the genus based on earlier revisions or studies of type specimens, we recognized 11 Rhynchostegiella species. Eight of these species were already described, but were recircumscribed. In particular, the widespread R. litorea is split into three species with narrower distribution ranges: R. litorea s.str. occurs across continental Europe, western Asia and Africa (excluding Macaronesia), while R. pseudolitorea sp. nov. and R. tubulosa sp. nov. are Macaronesian and Aegean‐Cypriotic endemics, respectively. The Macaronesian endemic R. macilenta is reduced to synonymy with R. teneriffae while the Macaronesian endemic status of R. bourgeana is re‐instated. Altogether, Rhynchostegiella thus includes four Macaronesian endemic species, namely R. azorica, R. bourgeana, R. pseudolitorea, and R. trichophylla, making it the bryophyte genus with the highest number of Macaronesian endemic species. Given the difficulties in identifying some of those species from morphology, as highlighted in the identification key presented here, we describe easy‐to‐use DNA “barcodes” that can be a useful tool for specimen identification when key morphological characters lead to uncertain identification.

Avoiding a second amniocentesis to corroborate prenatal diagnosis by using refrigerated samples*

Objective: This study is aimed to probe the usefulness of refrigerated aliquots of amniotic fluid to be used for fluorescence in situ hybridization (FISH) in order to perform an accurate prenatal diagnosis avoiding the risk related to an additional amniocentesis procedure and the psychological stress to the pregnant woman and her family.Methods: Non-cultured amniotic fluid (AF) samples were analyzed by FISH. Such samples were divided into two groups. The first one included fresh collected AF (FCAF, N = 30). The second one included refrigerated samples of AF (RSAF, N = 12) to corroborate uncertain chromosome aberration identification obtained by conventional methods. Sample refrigeration did not exceed 18 days.Results: No differences were found between groups. In the RSAF group, three cases of chromosomal mosaicismo and seven cases of pseudomosaicism were corroborated. No alteration adjudicated to aberrant chromosomal line presence was found in born children according to genetic specialists’ criteria. In the two remaining cases, applied procedure allowed elucidating fetal sex in one case and the origin of a marker chromosome in the other.Conclusions: Amniocytes obtained from RAFS are a useful biological material to be assayed by FISH, achieving an accurate prenatal diagnosis and avoiding an additional amniocentesis.