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Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.

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In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

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Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.

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Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

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The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.

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Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

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A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol.

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Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

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Abstract Emotional dysregulation (ED) is related to problems in understanding, perceiving, and regulating emotions. The aim is to find the psychometric properties of an instrument that measures ED and classifies the high/low ED group membership with the least possible error. For statistical purposes (factor analysis), two independent samples of males and females (n1 = 476) and (n2 = 562) were obtained, with ages sample 1 (15 -19 years; M= 15.8; SD=0.71) and sample 2 (15-19 years; M=15.6; SD= 0.69). Three factors were formed by sex, males with 14 items and females with 13 items, each loading on a single factor (total α=0.71 - 0.78 ɷ =0.67- 0.79 females; α= 0.70 - 0.79 ɷ=0.73 - 0.75 males) and good fit indices. In sum, a validated cut version instrument (DERSR-B), a risk screening instrument, was obtained.

Resumen La desregulación emocional (DE) se relaciona con problemas para comprender, percibir y regular las emociones. Determinar las propiedades psicométricas de un instrumento que mide DE y que clasifica con el menor error posible la pertenencia de grupo alto/bajo de DE se propuso como el objetivo de este estudio. Para propósitos estadísticos (análisis factoriales) se obtuvieron dos muestras independientes de hombres y mujeres (n1 = 476) y (n2 = 562) respectivamente, con edades para muestra 1 (15-19 años; M= 15.8; DE= 0.71) y muestra 2 (15-19 años; M=15.6; DE= 0.69). Se obtuvieron tres factores por sexo, hombres con 14 ítems y mujeres con 13 ítems cada uno cargando en un solo, un único factor (total α =0.71 - 0.78 ɷ =0.67- 0.79 mujeres; α=0.70 - 0.79 ɷ=0.73-0.75 hombres) y con índices de ajuste aceptables. Se obtuvo un instrumento válido en versión corta de detección rápida de riesgo de Desregulación Emocional (DERSR-B).

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The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

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Gears are reliable and robust elements that are found in any power transmission system. However, gears are prone to present incipient faults, such as wear, since they are constantly subjected to contact forces. Due to gears playing a key role in many industrial processes, it is important to develop condition monitoring strategies that ensure the proper functioning of the related power transmission system and the overall components. In this regard, the data on entropy provide relevant information that allow us to identify and quantify the effect of different wear levels in gears. Therefore, in this work, we proposed the use of seven entropy-related features to perform the identification of different wear severities in a gearbox. The novelty of this proposal lies in the use of the entropy features to carry out a high-performance characterization of the available vibration signals that are acquired from experimental tests. The novelty of this proposal lies in the fusion of three different techniques: entropy features, linear discriminant analysis, and artificial neural networks to obtain a machine learning approach for improving the detection of different wear severities in gears compared to other reported methodologies. This situation is achieved due to the high-performance characterization of the available vibration signals that are acquired from experimental tests. Additionally, the entropy features are subjected to a feature space transformation by means of linear discriminant analysis to obtain a 2D representation and, finally, the set of features extracted by linear discriminant analysis are used as inputs of a neural network-based classifier to determine the severity of wear that is present in the gears. The proposed methodology is validated and compared with a conventional statistical approach to show the improvement in the classification.

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Abstract Introduction The spatial auditory system, though developed at birth, attains functional maturity in the late childhood (12 years). Spatial changes during childhood affect navigation in the environment and source segregation. Accommodation of a new skill through learning, especially during childhood, can expedite this process. Objective To explore the auditory spatial benefits of abacus training on psychoacoustic metrics in children. The study also aimed to identify the most sensitive metric to abacus training related changes in spatial processing, and utilize this metric for a detailed spatial error profiling. Methods A standard group comparison analysis with 90 participants divided into three groups: I: children with abacus training (C-AT); II: children with no training (C-UT); III: adults with no training (A-UT). The groups underwent a series of psychoacoustic tests, such as interaural time difference (ITD), interaural level difference (ILD), and virtual auditory space identification (VASI), as well as perceptual tests such as the Kannada version of the speech, spatial, and quality questionnaire (K-SSQ). Results Significant group differences were observed in the multivariate analysis of variance (MANOVA) and post-hoc tests, with the C-AT group showing significantly lower ILD scores (p = 0.01) and significantly higher VASI scores (p < 0.001) compared to the CUT group, which is indicative of better spatial processing abilities in the former group. The discriminant function (DF) analyses showed that the VASI was the most sensitive metric for training-related changes, based on which elaborate error analyses were performed. Conclusions Despite the physiological limits of the immature neural framework, the performance of the C-AT group was equivalent to that of untrained adults on psychoacoustic tests, which is reflective of the positive role of abacus training in expediting auditory spatial maturation.

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BACKGROUND: Obesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes. METHODS: Principal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation. RESULTS: Although no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm-1 peak [νas(PO2-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm-1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia  and the control group. CONCLUSIONS: Our results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

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Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.

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Introduction The spatial auditory system, though developed at birth, attains functional maturity in the late childhood (12 years). Spatial changes during childhood affect navigation in the environment and source segregation. Accommodation of a new skill through learning, especially during childhood, can expedite this process. Objective To explore the auditory spatial benefits of abacus training on psychoacoustic metrics in children. The study also aimed to identify the most sensitive metric to abacus training related changes in spatial processing, and utilize this metric for a detailed spatial error profiling. Methods A standard group comparison analysis with 90 participants divided into three groups: I: children with abacus training (C-AT); II: children with no training (C-UT); III: adults with no training (A-UT). The groups underwent a series of psychoacoustic tests, such as interaural time difference (ITD), interaural level difference (ILD), and virtual auditory space identification (VASI), as well as perceptual tests such as the Kannada version of the speech, spatial, and quality questionnaire (K-SSQ). Results Significant group differences were observed in the multivariate analysis of variance (MANOVA) and post-hoc tests, with the C-AT group showing significantly lower ILD scores ( p = 0.01) and significantly higher VASI scores ( p <0.001) compared to the C-UT group, which is indicative of better spatial processing abilities in the former group. The discriminant function (DF) analyses showed that the VASI was the most sensitive metric for training-related changes, based on which elaborate error analyses were performed. Conclusions Despite the physiological limits of the immature neural framework, the performance of the C-AT group was equivalent to that of untrained adults on psychoacoustic tests, which is reflective of the positive role of abacus training in expediting auditory spatial maturation.

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Abstract Worm infections pose a significant challenge to goat farming in the tropics. While individual variations in the animals' response to this disease are observed, understanding its genetic component is crucial for establishing effective herd production management, prioritizing the selection of goats with higher resistance to parasitism. This study aimed to assess goat response to worm infection under natural field conditions using data on eggs per gram of feces (EPG), body condition score (BCS), and conjunctival mucosa coloration (FAMACHA©). Cluster analysis and artificial intelligence (AI) techniques were applied to 3,839 data points from 200 individuals in an experimental goat herd in Piauí, Brazil. The study considered the phenotypic expression of resistance, sensitivity, and resilience to worm infection as responses to parasitism. Three clustering methods, namely Ward, Average, and k-means, were employed and compared with fuzzy logic obtained through the CAPRIOVI web software. The analysis revealed statistically significant differences (P<0.05) between the groups of animals classified as resistant, resilient, and sensitive to parasitism. Pregnancy and peripartum were identified as stages of heightened sensitivity to parasitism (P<0.05). Among the clustering techniques, traditional statistical methods exhibited excellent performance, with an overall accuracy percentage exceeding 90.00%. In contrast, CAPRIOVI's fuzzy logic demonstrated lower overall accuracy (77.00%). The clustering methods showed similar efficiency, but differed in terms of the distribution of animals per group, with a tendency towards greater numbers in the resistant category. Fuzzy logic circumvented this limitation by enabling the formation of groups tailored to meet the producer's interests, adding consistency in terms of the animals' response to worm infection. This finding highlights the potential of the software for goat health management.

Resumo A incidência de verminose é um dos principais obstáculos para a caprinocultura nos trópicos. A variação individual da resposta do animal à enfermidade existe, mas precisa ser determinado o seu componente genético e estabelecer o manejo zootécnico dos rebanhos, priorizando a seleção de animais mais resistente ao parasitismo. Objetivou-se nesse estudo avaliar a resposta de cabras à incidência de verminose sob condições de infecção natural a campo, com informações de ovos por grama de fezes (OPG), escore da condição corporal (ECC) e grau de coloração da mucosa conjuntiva (FAMACHA©), recorrendo a utilização de análise de agrupamento e a aplicação de inteligência artificial (IA). Foram utilizadas 3.839 informações de 200 indivíduos em um rebanho experimental de caprinos no Piauí. Considerou-se como resposta ao parasitismo a expressão fenotípica de resistência, sensibilidade e resiliência a verminose, submetidos a três métodos de agrupamento: Ward, Average e K-means, comparado com a lógica Fuzzy, obtidos com o software web CAPRIOVI. Os resultados demonstraram que os grupos de animais resistente, resiliente e sensível ao parasitismo foram estatisticamente distintos (P<0,05). As cabras durante a gestação e o periparto foram identificadas como fases de maior sensibilidade ao parasitismo (P<0,05). O CAPRIOVI aplica a lógica Fuzzy e apresentou o menor percentual de acerto global (77,00%), enquanto os métodos estatísticos tradicionais se destacaram com percentual de acerto global superior a 90,00%, demonstrando excelência estatística com esse fim. Os métodos de agrupamentos apresentaram semelhança na eficiência, mas diferiram quanto à distribuição de animais por agrupamento, com tendência de maior quantidade na categoria resistente. A aplicação da lógica Fuzzy contornou essa limitação ao direcionar a formação dos grupos visando atender o interesse do produtor, inserindo consistência em termos de resposta dos animais a verminose, qualificando o software com potencial para adequação ao manejo sanitário de caprinos.

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Physids belong to Class Gastropoda; Phylum Mollusca have important position in food web and act as bio indicators, pests and intermediate host. Being resistant these are called cockroaches of malacology. Physid snails were collected from different water bodies of Faisalabad (Punjab) and were identified up to species using morphological markers. The morphometry of the specimens was carried out with the help of a digital Vernier caliper in millimeters (mm) using linear measurement of shell characters. Linear regression analysis of the AL/SW ratio vs AL and SL/SW ratiovs AL indicated that allometric growth exists only in Physa acuta when compared with P.gyrina and P. fontinalis. This study will lead to assess the status of the Physid species in Central Punjab. The Principal component analysis shows that the Component 1 (Shell Length) and component 2 (Shell Width) are the most prolific components and nearly 80 percent of the identification. The distance between P. acuta and P. fontinalis is 5.4699, P. acuta and P. gyrina is 7.6411, P. fontinalis and P. gyrina is 16.6080 showing that P. acuta resembles with P. fontinalis, and both these specimens donot resemble with P. gyrina. P.acuta is an invasive species and shows bioactivity making it a potent candidate for bioactive substances.(AU)

Os físidos pertencem à classe Gastropoda; o filo Mollusca possui importante posição na teia alimentar e atua como bioindicador, praga e hospedeiro intermediário. Por serem resistentes, são chamadas baratas de malacologia. Os caramujos físidos foram coletados em diferentes corpos dágua de Faisalabad (Punjab) e identificados até as espécies por meio de marcadores morfológicos. A morfometria dos corpos de prova foi realizada com auxílio de paquímetro digital Vernier em milímetros (mm) por meio de medida linear dos caracteres da casca. A análise de regressão linear da razão AL / SW vs. AL e razão SL / SW vs. AL indicou que o crescimento alométrico existe apenas em Physa acuta quando comparado com P. gyrina e P. fontinalis. Este estudo levará a avaliar a situação das espécies de físido no Punjab Central. A análise do componente principal mostra que o componente 1 (comprimento da casca) e o componente 2 (largura da casca) são os componentes mais prolíficos e quase 80% da identificação. A distância entre P. acuta e P. fontinalis é 5,4699, P. acuta e P. gyrina é 7,6411, P. fontinalis e P. gyrina é 16,6080, mostrando que P. acuta se assemelha a P. fontinalis, e ambos os espécimes não se parecem com P. gyrina. P. acuta é uma espécie invasora e apresenta bioatividade, tornando-se uma candidata potente para substâncias bioativas.(AU)

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Physids belong to Class Gastropoda; Phylum Mollusca have important position in food web and act as bio indicators, pests and intermediate host. Being resistant these are called cockroaches of malacology. Physid snails were collected from different water bodies of Faisalabad (Punjab) and were identified up to species using morphological markers. The morphometry of the specimens was carried out with the help of a digital Vernier caliper in millimeters (mm) using linear measurement of shell characters. Linear regression analysis of the AL/SW ratio vs AL and SL/SW ratiovs AL indicated that allometric growth exists only in Physa acuta when compared with P.gyrina and P. fontinalis. This study will lead to assess the status of the Physid species in Central Punjab. The Principal component analysis shows that the Component 1 (Shell Length) and component 2 (Shell Width) are the most prolific components and nearly 80 percent of the identification. The distance between P. acuta and P. fontinalis is 5.4699, P. acuta and P. gyrina is 7.6411, P. fontinalis and P. gyrina is 16.6080 showing that P. acuta resembles with P. fontinalis, and both these specimens donot resemble with P. gyrina. P.acuta is an invasive species and shows bioactivity making it a potent candidate for bioactive substances.

Os físidos pertencem à classe Gastropoda; o filo Mollusca possui importante posição na teia alimentar e atua como bioindicador, praga e hospedeiro intermediário. Por serem resistentes, são chamadas baratas de malacologia. Os caramujos físidos foram coletados em diferentes corpos d’água de Faisalabad (Punjab) e identificados até as espécies por meio de marcadores morfológicos. A morfometria dos corpos de prova foi realizada com auxílio de paquímetro digital Vernier em milímetros (mm) por meio de medida linear dos caracteres da casca. A análise de regressão linear da razão AL / SW vs. AL e razão SL / SW vs. AL indicou que o crescimento alométrico existe apenas em Physa acuta quando comparado com P. gyrina e P. fontinalis. Este estudo levará a avaliar a situação das espécies de físido no Punjab Central. A análise do componente principal mostra que o componente 1 (comprimento da casca) e o componente 2 (largura da casca) são os componentes mais prolíficos e quase 80% da identificação. A distância entre P. acuta e P. fontinalis é 5,4699, P. acuta e P. gyrina é 7,6411, P. fontinalis e P. gyrina é 16,6080, mostrando que P. acuta se assemelha a P. fontinalis, e ambos os espécimes não se parecem com P. gyrina. P. acuta é uma espécie invasora e apresenta bioatividade, tornando-se uma candidata potente para substâncias bioativas.

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RESUMO

Abstract Physids belong to Class Gastropoda; Phylum Mollusca have important position in food web and act as bio indicators, pests and intermediate host. Being resistant these are called cockroaches of malacology. Physid snails were collected from different water bodies of Faisalabad (Punjab) and were identified up to species using morphological markers. The morphometry of the specimens was carried out with the help of a digital Vernier caliper in millimeters (mm) using linear measurement of shell characters. Linear regression analysis of the AL/SW ratio vs AL and SL/SW ratio vs AL indicated that allometric growth exists only in Physa acuta when compared with P.gyrina and P. fontinalis. This study will lead to assess the status of the Physid species in Central Punjab. The Principal component analysis shows that the Component 1 (Shell Length) and component 2 (Shell Width) are the most prolific components and nearly 80 percent of the identification. The distance between P. acuta and P. fontinalis is 5.4699, P. acuta and P. gyrina is 7.6411, P. fontinalis and P. gyrina is 16.6080 showing that P. acuta resembles with P. fontinalis, and both these specimens donot resemble with P. gyrina. P.acuta is an invasive species and shows bioactivity making it a potent candidate for bioactive substances.

Resumo Os físidos pertencem à classe Gastropoda; o filo Mollusca possui importante posição na teia alimentar e atua como bioindicador, praga e hospedeiro intermediário. Por serem resistentes, são chamadas baratas de malacologia. Os caramujos físidos foram coletados em diferentes corpos dágua de Faisalabad (Punjab) e identificados até as espécies por meio de marcadores morfológicos. A morfometria dos corpos de prova foi realizada com auxílio de paquímetro digital Vernier em milímetros (mm) por meio de medida linear dos caracteres da casca. A análise de regressão linear da razão AL / SW vs. AL e razão SL / SW vs. AL indicou que o crescimento alométrico existe apenas em Physa acuta quando comparado com P. gyrina e P. fontinalis. Este estudo levará a avaliar a situação das espécies de físido no Punjab Central. A análise do componente principal mostra que o componente 1 (comprimento da casca) e o componente 2 (largura da casca) são os componentes mais prolíficos e quase 80% da identificação. A distância entre P. acuta e P. fontinalis é 5,4699, P. acuta e P. gyrina é 7,6411, P. fontinalis e P. gyrina é 16,6080, mostrando que P. acuta se assemelha a P. fontinalis, e ambos os espécimes não se parecem com P. gyrina. P. acuta é uma espécie invasora e apresenta bioatividade, tornando-se uma candidata potente para substâncias bioativas.

RESUMO

Abstract Physids belong to Class Gastropoda; Phylum Mollusca have important position in food web and act as bio indicators, pests and intermediate host. Being resistant these are called cockroaches of malacology. Physid snails were collected from different water bodies of Faisalabad (Punjab) and were identified up to species using morphological markers. The morphometry of the specimens was carried out with the help of a digital Vernier caliper in millimeters (mm) using linear measurement of shell characters. Linear regression analysis of the AL/SW ratio vs AL and SL/SW ratio vs AL indicated that allometric growth exists only in Physa acuta when compared with P.gyrina and P. fontinalis. This study will lead to assess the status of the Physid species in Central Punjab. The Principal component analysis shows that the Component 1 (Shell Length) and component 2 (Shell Width) are the most prolific components and nearly 80 percent of the identification. The distance between P. acuta and P. fontinalis is 5.4699, P. acuta and P. gyrina is 7.6411, P. fontinalis and P. gyrina is 16.6080 showing that P. acuta resembles with P. fontinalis, and both these specimens donot resemble with P. gyrina. P.acuta is an invasive species and shows bioactivity making it a potent candidate for bioactive substances.

Resumo Os físidos pertencem à classe Gastropoda; o filo Mollusca possui importante posição na teia alimentar e atua como bioindicador, praga e hospedeiro intermediário. Por serem resistentes, são chamadas baratas de malacologia. Os caramujos físidos foram coletados em diferentes corpos d'água de Faisalabad (Punjab) e identificados até as espécies por meio de marcadores morfológicos. A morfometria dos corpos de prova foi realizada com auxílio de paquímetro digital Vernier em milímetros (mm) por meio de medida linear dos caracteres da casca. A análise de regressão linear da razão AL / SW vs. AL e razão SL / SW vs. AL indicou que o crescimento alométrico existe apenas em Physa acuta quando comparado com P. gyrina e P. fontinalis. Este estudo levará a avaliar a situação das espécies de físido no Punjab Central. A análise do componente principal mostra que o componente 1 (comprimento da casca) e o componente 2 (largura da casca) são os componentes mais prolíficos e quase 80% da identificação. A distância entre P. acuta e P. fontinalis é 5,4699, P. acuta e P. gyrina é 7,6411, P. fontinalis e P. gyrina é 16,6080, mostrando que P. acuta se assemelha a P. fontinalis, e ambos os espécimes não se parecem com P. gyrina. P. acuta é uma espécie invasora e apresenta bioatividade, tornando-se uma candidata potente para substâncias bioativas.

Assuntos