Multivariate Discriminant Analysis Research Articles

A Study on Financial Risk Early Warning in Pharmaceutical Companies: The Case of Ruikang Pharmaceutical

This paper analyzes the financial data of Ruikang Pharmaceutical from 2021 to 2023 through the Z-score model. This calculation method makes the multivariate linear discriminant analysis method proposed by the American scholar Edward Eltman. This approach evaluates the financial health of a firm by combining multiple financial indicators in a weighted manner to arrive at a composite indicator, Z-score. Systematically analyze the operation status and bankruptcy of the enterprise, so as to evaluate the potential risks that the company may face in financial aspects, and warn the possible problems in advance, so as to help the enterprise take corresponding measures to avoid risks, improve the competitiveness and sustainable development of the enterprise, and ensure the financial health and stable development of the company. It can improve the ability of pharmaceutical companies to provide high-level and high-quality services, provide reasonable fund management programs for enterprises themselves, and meet the market demand. Through the calculation and analysis results, it can be seen that there are certain financial risks in Ruikang Pharmaceutical, and Ruikang Pharmaceutical has been on the edge of bankruptcy for three years. Finally, this paper puts forward the corresponding solutions and countermeasures to improve the vitality of the company and make it develop for the better.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations

BackgroundSalicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate.ResultsA trial and validation set of 96 and 24 plants from 2 populations confirmed the efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding.ConclusionsThe CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

Clinical Cues for the Early Diagnosis of Transthyretin-Related Polyneuropathy.

The estimated prevalence of hereditary transthyretin-related familial amyloid polyneuropathy (TTR-FAP) and the small number of known patients in Germany indicate that many patients with TTR-FAP remain undiagnosed, and may instead be classified as "idiopathic." The aim of this study was to identify biomarkers for detecting TTR-FAP among a cohort of patients with idiopathic polyneuropathy (PNP). Clinical evaluations (including the Neuropathy Impairment Score and Neuropathy Disability Score), nerve conduction studies (NCSs), quantitative sensory testing, and autonomic function tests were performed on 23 patients with TTR-FAP and 89 with idiopathic PNP. Discriminant analysis was then performed to identify variables useful for predicting TTR-FAP. Patients with TTR-FAP had paresis of the finger and thumb muscles, and reduced vibration perception and increased pressure pain in the upper and lower extremities. The NCSs showed that action potentials were smaller in the median, ulnar (both motor and sensory), and sural nerves in TTR-FAP. The sensory nerve conduction velocity was also reduced in the ulnar nerve. Autonomic neuropathy was confirmed by reduced sympathetic skin responses in the hands and feet in TTR-FAP. Multivariate discriminant analysis revealed that finger abduction strength, sensory ulnar nerve action potential amplitude, and vibration detection and pressure pain thresholds in the upper extremities were sufficient to correctly identify TTR-FAP in 81.3% of cases. Detailed clinical and neurophysiological investigations of standard parameters in the upper limb may help to identify the otherwise-rare TTR-FAP.

Metabolomics based on GC-MS combined with chemometrics for geographical discrimination of garlic (Allium sativum L.)

Ensuring the geographic origin of products helps protect consumers against fraud and mislabeling. This study investigates the metabolomic profiles of garlic (Allium sativum L.) samples from Brazil and China using gas chromatography-mass spectrometry (GC-MS). Multivariate analysis Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal PLS-DA (Ortho PLS-DA) revealed clear metabolic distinctions between the two sample groups with high classification accuracy. Brazilian garlic samples showed higher levels of sulfur compounds, such as allyl propyl sulfide, cysteine sulfonic acid, and S-carboxymethyl-L-cysteine, which contribute to its pungency and potential health benefits. Amino acids L-arginine, L-glutamic acid, and L-cysteine were also significantly increased in garlic from Brazil. In contrast, Chinese garlic exhibited higher intensities of sugars (e.g., D-fructose, D-mannose) and essential amino acids (L-tryptophan, L-valine, and L-leucine). These differences in metabolism are the result of distinct metabolic profiles. GC-MS-based untargeted metabolomic and chemometric analysis were able to differentiate the geographical origin of Brazilian and Chinese garlics and remain valuable to food authentication and quality control. Further studies are needed to confirm the biomarkers for origin verification here stated.

Quantifying phenotypic variability of indigenous chickens using morphometric traits by applying multivariate analysis: Input for sustainable rural chicken farming

Despite environmentally influenced performance traits in farm animals, variations in morphometric characters reflect unswerving genetic effects. In this study, body weight and body dimension measurements of indigenous chickens reared in four rural communities were investigated using multivariate statistical procedures to elucidate the existence of phenotypic variability between chicken populations. The study districts were selected based on higher indigenous chicken population, geographic distance, and agro-ecological distinctiveness. Eleven morphometric traits were measured on 1060 adult chickens from the districts. The results revealed that there were significant (p < 0.0001) variations between populations across districts. Significantly (P < 0.0001) higher mean values for morphometric traits were obtained in chickens reared in the Hula (highland district) than in those of the three districts. Likewise, multivariate discriminant analysis showed that most of the variables had significant power to differentiate the chicken populations into separate groups. Accordingly, three discriminant functions were extracted, of which the first two explained 95.6 % of the total variances in the populations. Moreover, in cluster analysis, chickens were differentiated into two sub-groups: Hula as a separate population, and Shebedino, Aleta Chuko and Boricha populations clustered together. Furthermore, the discriminant analysis correctly assigned 56.7 %, 55.1 %, 51.5 %, and 53.3 % to their source populations of Hula, Aleta Chuko, Shebedino, Hula, and Boricha, respectively, showing high mixing and mobility of chickens across neighboring districts. Therefore, this study confirmed the existence of significant morphometric variability between the studied populations, which could be used as a valuable source of information for selective breeding and sustainable use of indigenous chickens, particularly for rural farmers who almost rely on phenotypic features to select breeding stocks. In addition, the higher morphometric measurements shown for Hula chickens should be confirmed by on-station evaluation, and further molecular tools should be applied to validate the genetic distinctiveness of the studied chicken populations.

Bankruptcy Prediction and Performance: the Influence of Quality Certification on Portuguese SMEs

Objective: To observe the existence of a relationship between quality certification and performance and bankruptcy prediction for Portuguese SMEs. Theoretical Reference: The search for quality and its certification, via the International Standard Organization (ISO), theoretically, is related to the going concern assumption, making it possible to enhance the company's economic and financial sustainability. Method: Multisectoral Multivariate Discriminant Analysis (ADM) models, considered the most efficient, were selected for application, as well as the commonly used Univariate Analysis indicators. In Bureau Van Dijk's SABI Database, we collected Portuguese SMEs, with financial information from 2014 to 2018, certified in the scope of quality, ISO 9000 or 9001, on 12/31/2018, of these, we focused on those with main activity in NACE D - Electricity, Gas, Steam, Hot and Cold Water and Cold Air or G - Wholesale and Retail Trade, Repair of Motor Vehicles and Motorcycles as they are the most important. The Sector Average Company was also collected for the same NACE via Banco de Portugal Sector Tables. Then, having applied the models and indicators under study, we compared the results obtained between them, in order to observe the possible effect of quality certification on financial performance. Results and Discussion: No implication of quality certification in improving or optimizing the performance or economic-financial position of entities is identified. Implications of the Research: Uncover the influence of quality certification on the economic-financial position of companies, allowing for deeper investigation into bankruptcy, sustainability and value creation. Originality/Value: The specificities of the sectors and business size, crises and increasing closures make it vital to identify what will influence performance and sustainability, as no studies have been identified on this topic, focusing on Portuguese SMEs, certified via ISO 9000 or 9001.

Upregulation of Apoptosis Related Genes in Clinically Normal Tongue Contralateral to Squamous Cell Carcinoma of the Oral Tongue, an Effort to Maintain Tissue Homeostasis

PurposeThe field cancerization concept indicates the presence of pre-cancerous changes in clinically normal tissue surrounding the tumor. In squamous cell carcinoma of the oral tongue (SCCOT) which is infrequently linked to human papillomavirus infection, we have previously reported that clinically normal tongue contralateral to tumor (NTCT) is molecularly abnormal. Here, combining our transcriptomic and genomic data, we aimed to investigate the contribution of molecular changes in NTCT to cancer development.MethodsMicroarray gene expression data of 14 healthy controls, 23 NTCT and 29 SCCOT samples were investigated to characterize transcriptional profiles in NTCT. Whole exome sequencing and RNA-sequencing data of paired NTCT and tumor samples from 15 SCCOT patients were used to study correlation between copy number variation and differential gene expression.ResultsUsing supervised multivariate partial least squares discriminant analysis, a total of 61 mRNAs that distinguish NTCT from healthy tongue were selected. Functional enrichment analysis of the 22 upregulated genes showed increased “positive regulation of nitrogen compound metabolic process” in NTCT. All 12 genes involved in this process have roles in apoptosis (anti- and/or pro-apoptotic). Compared to healthy controls, Zinc Finger Protein 395 (ZNF395), a pro-apoptotic tumor suppressor located on chromosome 8p, was the only gene showing increased mRNA level in NTCT whereas decreased in SCCOT. Given the frequent loss of chromosome 8p in SCCOT, the impact of ZNF395 copy number variation on gene expression was further examined, revealing a positive correlation between copy number and mRNA level (correlation coefficient = 0.572, p < 0.001).ConclusionNTCT is susceptible to malignant transformation, where tissue homeostasis is maintained at least partly through regulation of apoptosis. Loss of the pro-apoptotic gene ZNF395 could thus initiate cancer development.

Predictive models of intent to repurchase based on customer data

As the business environment becomes ever more complex, predicting future buying patterns has become increasingly important. Moreover, there has been growth in the number of consumers focusing on health and self-care given rapid societal and economic changes. This shift is leading to heightened interest in athleisure wear and an increase in sales volume in this domain. It is accordingly necessary for companies in this field to establish marketing strategies based on predicting consumers’ future purchases to maintain continuous growth and competitiveness. This paper surveyed 400 consumers who purchased athleisure wear and established a predictive model for consumer buying behavior using Multivariate Discriminant Analysis. This paper found that a brand's authenticity and the purchaser’s involvement in sports were significant factors prediction customers’ repurchase behavior. Companies should accordingly engage in authentic business practices and consider clothing designs and marketing strategies that can connect consumers' daily lives with their sports activities.

Spatial colocalization and combined survival benefit of natural killer and CD8 T cells despite profound MHC class I loss in non-small cell lung cancer

BackgroundMajor histocompatibility complex class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and...

Sex estimation from skull measurements of a contemporary Japanese population using three-dimensional computed tomography images.

In this study, we assessed the sexual dimorphism of the contemporary Japanese skull and established sex discriminant function equations based on cranial measurements using three-dimensional (3D) computed tomography (CT) images. The CT images of 263 corpses (142 males, 121 females) that underwent postmortem CT scanning and subsequent forensic autopsy were evaluated. Twenty-one cranial measurements were obtained from 3D CT reconstructed images, which extracted only bone data. We performed descriptive statistics and discriminant function analyses for the measurements. Nineteen measurements were significantly larger in males, suggesting sexual dimorphism of the Japanese skulls. Univariate discriminant function analyses using these measurements showed a sex classification accuracy of 57.8-88.2%, and bizygomatic breadth provided the highest correct prediction rate. Multivariate discriminant function analyses offered the most accurate model using seven variables with an estimation rate of 93.9%. Our results suggest that cranial measurements based on 3D CT images may help in the sex estimation of unidentified bodies in a contemporary Japanese population.

A multimodal approach for fostering knowledge transfer in engineering design activity: an eye-tracking and fNIRS study

Knowledge transfer in engineering design activity involves effectively applying prior practical experience in novel contexts. While related works fostering knowledge transfer focus on educational practices and experience generalisation, recent advancements enable us to analyze its complex cognitive processes using neuroimaging technology. This paper proposes a multimodal approach for facilitating knowledge transfer in engineering design, utilising functional near-infrared spectroscopy (fNIRS) and eye-tracking technologies based on neurocognitive profiling. We conducted knowledge transfer experiments using mixed and extended design tasks. Meanwhile, The analysis focused on gaze duration, gaze counts, brain region activation levels, functional connectivity, and effective connectivity of the participants. Finally, we employed stepwise linear regression to identify significant and potential associated factors of knowledge transfer. By combining eye-tracking predictors and brain network predictors, a multivariate linear discriminant analysis (LDA) significantly differentiated between participants in near-transferred and far-transferred states, achieving an accuracy of 91.16% using leave-one-out cross-validation. The ‘basic-supplementary-target task’ method effectively improved the knowledge transfer performance by 27.6%. Our findings demonstrate the efficacy of the proposed multimodal approach in facilitating knowledge transfer in engineering design activities.

Insights into the pathophysiology and response of persistent spinal pain syndrome type 2 to spinal cord stimulation: a human genome-wide association study

BackgroundSpinal cord stimulation (SCS) provides pain relief for some patients with persistent spinal pain syndrome type 2 (PSPS 2), but the precise mechanisms of action and prognostic factors for a...

Factors affecting deposit formation in foul condensate stripping systems

In kraft pulp mills, foul condensates are often steam-stripped to produce clean condensate for use as process water. The formation of organic deposits in the stripped condensate is a common problem. A systematic study was conducted to examine the deposit composition and the most likely operating parameters responsible for stripped condensate contamination experienced at a kraft mill in Brazil. Daily averaged data of 170 operating parameters over a 15-month period were analyzed by means of multivariate discriminant analysis and random forest classification analysis. The results showed that the deposit formation is related to high temperature, pressure, and dry solids operations in various evaporator effects. These conditions, combined with the poor demisting efficiency in these effects, may have increased black liquor carryover mist in the vapor. Deposit formation also appeared to be related to increased throughput of the foul condensate stripping system and increased pressure in the stripper. Results of Fourier transform infrared spectroscopy (FTIR) and pyrolysis-gas chromatography mass spectrometry (Py-GCMS) analyses show that the deposit consists of mostly organic matter that likely originated from wood extractives and lignin.

Spatial colocalization and combined survival benefit of natural killer and CD8 T cells despite profound MHC class I loss in non-small cell lung cancer.

MHC class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and although previous work indicated their presence at NSCLC margins, they were functionally impaired. Within, we evaluated whether NK cell and CD8 T cell infiltration and activation vary with MHC-I expression. We used single-stain immunohistochemistry (IHC) and Kaplan-Meier analysis to test the effect of NK cell and CD8 T cell infiltration on overall and disease-free survival. To delineate immune covariates of MHC-I-disparate lung cancers, we used multiplexed immunofluorescence (mIF) imaging followed by multivariate statistical modeling. To identify differences in infiltration and intercellular communication between IFNγ-activated and non-activated lymphocytes, we developed a computational pipeline to enumerate single cell neighborhoods from mIF images followed by multivariate discriminant analysis. Spatial quantitation of tumor cell MHC-I expression revealed intra- and inter-tumoral heterogeneity, which was associated with the local lymphocyte landscape. IHC analysis revealed that high CD56+ cell numbers in patient tumors were positively associated with disease-free survival (DFS) (HR=0.58, p=0.064) and overall survival (OS) (HR=0.496, p=0.041). The OS association strengthened with high counts of both CD56+ and CD8+ cells (HR=0.199, p<1×10-3). mIF imaging and multivariate discriminant analysis revealed enrichment of both CD3+CD8+ T cells and CD3-CD56+ NK cells in MHC-I-bearing tumors (p<0.05). To infer associations of functional cell states and local cell-cell communication, we analyzed spatial single cell neighborhood profiles to delineate the cellular environments of IFNγ+/- NK cells and T cells. We discovered that both IFNγ+ NK and CD8 T cells were more frequently associated with other IFNγ+ lymphocytes in comparison to IFNγ- NK cells and CD8 T cells (p<1×10-30). Moreover, IFNγ+ lymphocytes were most often found clustered near MHC-I+ tumor cells. Tumor-infiltrating NK cells and CD8 T cells jointly affected control of NSCLC tumor progression. Co-association of NK and CD8 T cells was most evident in MHC-I-bearing tumors, especially in the presence of IFNγ. Frequent co-localization of IFNγ+ NK cells with other IFNγ+ lymphocytes in near-neighbor analysis suggests NSCLC lymphocyte activation is coordinately regulated.

Conjugated linoleic acid (CLA) modulates bovine peripheral blood mononuclear cells (PBMC) proteome in vitro

Conjugated linoleic acid (CLA) is a group of natural isomers of the n-6 polyunsaturated fatty acid (PUFA) linoleic acid, exerting biological effects on cow physiology. This study assessed the impact of the mixture 50:50 (vol:vol) of CLA isomers (cis-9, trans-11 and trans-10, cis-12) on bovine peripheral blood mononuclear cells (PBMC) proteome, identifying 1608 quantifiable proteins. A supervised multivariate statistical analysis, sparse variant partial least squares – discriminant analysis (sPLS-DA) for paired data identified 407 discriminant proteins (DP), allowing the clustering between the CLA and controls. The ProteINSIDE workflow found that DP with higher abundance in the CLA group included proteins related to innate immune defenses (PLIN2, CD36, C3, C4, and AGP), with antiapoptotic (SERPINF2 and ITIH4) and antioxidant effects (HMOX1). These results demonstrated that CLA modulates the bovine PBMC proteome, supports the antiapoptotic and immunomodulatory effects observed in previous in vitro studies on bovine PBMC, and suggests a cytoprotective role against oxidative stress. SignificanceIn this study, we report for the first time that the mixture 50:50 (vol:vol) of cis-9, trans-11, and trans-10, cis-12-CLA isomers modulates the bovine PBMC proteome. Our results support the immunomodulatory and antiapoptotic effects observed in bovine PBMC in vitro. In addition, the present study proposes a cytoprotective role of CLA mixture against oxidative stress. We suggest a molecular signature of CLA treatment based on combining a multivariate sparse discriminant analysis and a clustering method. This demonstrates the great value of sPLS-DA as an alternative option to identify discriminant proteins with relevant biological significance.

Distribution of Elements in Durum Wheat Seed and Milling Products: Discrimination between Cultivation Methods through Multivariate Data Analysis.

Many staple foods originate from durum wheat and its milling products; because of this, it is very important to know their characteristics. This study investigates elemental contents in these products and if differences exist because of organic farming. The concentrations of 28 elements in the whole seed and in milling products, that is, bran, semolina and flour, of durum wheat, were determined through ICP-OES. The wheats were grown under conventional or organic agronomic practices to verify the possibility of discriminating, using the elemental content, between products coming from one or the other practice. The elements were more abundant in the outer layer of the seed, the bran, but most of them were also present in the others. Traces of Sb were present only in 3% of the samples, while traces of Tl were detected in approximately half of the seed and bran samples but not in other samples. The absence of an element was more characteristic of specific products, e.g., most semolina and flour lacked Co, while other elements showed small differences between products from organic and conventional cultivation or between different milling products, which was the case, for example, for traces of Ag, B, and V. The concentrations of these elements were coupled with multivariate discriminant analysis, specifically PLS-DA, to identify the cultivation provenance of the milled products. A few elements, although different for each product, are sufficient to attain precision and accuracy of classification close to 1; small differences exist for different products. The worst is flour, where the predicted precision and accuracy are 0.92, although using only three elements: B, K, and Se. Semolina attains perfect prediction when also adding to the three previous elements, Ag, Cd, and Cu. Further elements are necessary for bran, while Fe and Mg replace K and Ag to classify seeds. In conclusion, five elements, B, Cd, Cu, K, and Se, are the most important in distinguishing between organic and conventional agriculture; these elements also permit some differentiation among products. The method could help in fraud prevention.

Revealing the effects of amino acid, organic acid, and phytohormones on the germination of tomato seeds under salinity stress.

Salinity accumulation poses a threat to the production and productivity of economically important crops such as tomatoes (Solanum lycopersicum L.). Currently, salt tolerance breeding programs have been limited by insufficient genetic and physiological knowledge of tolerance-related traits and a lack of an efficient selection domain. For that purpose, we aimed to determine the ability of tomato cultivars to tolerate salt based on seed traits by multiple biochemical pathways. First, we tested three tomato cultivars according to their response to different sodium chloride (NaCl) concentrations (0, 6.3, 9.8, 13.0, and 15.8 dS m-1) and then we analysed their amino acids, organic acids, and phytohormones. Considering the results of germination traits, it is possible to conclude that cultivar H-2274 was more tolerant to salt stress than others. As a result, multivariate discriminant analysis including principal component analysis and two-way hierarchical clustering analyses were constructed and demonstrated that tomato cultivars were separated from each other by the amino acid, organic acid, and phytohormone contents. Considering germination traits of tomato seeds, cv. 'H-2274' was more tolerant to salinity than others depending on high proline (29 pmol µl-1) and citric acid (568 ng µl-1) assays. Biochemical variability offers a valuable tool for investigating salt tolerance mechanisms in tomatoes, and it will be appreciated to find high-tolerant tomato cultivar(s) to saline conditions. Also, the findings of this study have significant potential for practical applications in agriculture, particularly in developing salt-tolerant tomato cultivars to enhance productivity in saline environments and address socio-economic challenges.

Assessing the reliability and accuracy of sex estimation models utilizing sternal morphometry derived from computed tomography in the Ghanaian population

Sex estimation models are specific to populations and cannot be generalized due to genetic and environmental variabilities. This retrospective cross-sectional study, conducted between January and September 2023 at the Tamale Teaching Hospital, included 119 (50.9 %) females and 115 (49.1 %) males aged 23–82 years. Measurements, including manubrium length (M), manubrium width (MW), sternal body length (B), combined manubrium and sternal body lengths (CL), corpus sterni width at first sternebrae (CSWS1), and corpus sterni width at third sternebrae (CSWS3), were obtained from Computerized Tomographic (CT) images of the sternum using DICOM Viewer, accurate to 0.1 cm. Subsequently, sternal area (SA) and sternal index (SI) were calculated. Univariable and stepwise multivariable discriminant function analysis (DFA) and logistic regression (LR) models were developed using a training sample (70 %), and cross-validation was performed on a holdout sample (30 %). Results showed that the linear measurements, excluding M, and sternal area were higher in males than females (P<0.001), while the sternal index was <50 % in males but >50 % in females (P<0.001). Univariable sex estimation accuracies, in cross-validation, ranged from 43.7 % to 92.9 % in DFA and 50.0–92.9 % in LR. For multivariable models, the accuracy ranges were 92.9–94.3 % in DFA and 91.6–93.0 % in LR. The sternal body length was the most accurate at 90.1 % in DFA and 90.2 % in LR, with lower sex bias (male-female) in LR than in DFA (-0.1 vs. 8.7). The sternum proves valuable for sex estimation, with sternal body length as the most accurate linear measurement. However, multivariable models, particularly LR, demonstrate higher accuracy compared to DFA.

Osteological profiling of femoral diaphysis and neck in aquatic, semiaquatic, and terrestrial carnivores and rodents: effects of body size and locomotor habits

The increased limb bone density documented previously for aquatic tetrapods has been proposed to be an adaptation to overcome buoyancy during swimming and diving. It can be achieved by increasing the amount of bone deposition or by reducing the amount of bone resorption, leading to cortical thickening, loss of medullary cavity, and compaction of trabecular bone. The present study examined the effects of locomotor habit, body size, and phylogeny on the densitometric, cross-sectional, and biomechanical traits of femoral diaphysis and neck in terrestrial, semiaquatic, and aquatic carnivores, and in terrestrial and semiaquatic rodents (12 species) by using peripheral quantitative computed tomography, three-point bending, and femoral neck loading tests. Groupwise differences were analyzed with the univariate generalized linear model and the multivariate linear discriminant analysis supplemented with hierarchical clustering. While none of the individual features could separate the lifestyles or species adequately, the combinations of multiple features produced very good or excellent classifications and clusterings. In the phocid seals, the aquatic niche allowed for lower femoral bone mineral densities than expected based on the body mass alone. The semiaquatic mammals mostly had high bone mineral densities compared to the terrestrial species, which could be considered an adaptation to overcome buoyancy during swimming and shallow diving. Generally, it seems that different osteological properties at the levels of mineral density and biomechanics could be compatible with the adaptation to aquatic, semiaquatic, or terrestrial niches.

Kidney and Liver Predictors of Adults Hospitalized with COVID-19 Infection

SARS-CoV-2 damages not only the lungs, but also the liver and kidney. Most critically ill COVID-19 patients have liver and kidney dysfunctions. The early identification of patients with COVID-19 who will develop severe or critical disease symptoms is important for delivering proper and early treatment. This research studies the correlation of liver and kidney function indexes and COVID-19 outcomes. Electronic medical record data from 391 patients diagnosed with COVID-19 in the COVID-19 Department, Galilee Medical Center, Nahariya, Israel were collected. Epidemiological, clinical, laboratory, and imaging variables were analyzed. The liver and kidney enzyme indexes were measured upon admission and discharge. A correlation between laboratory levels and severity and mortality of COVID-19 patients was undertaken. This study included 391 COVID-19 patients, 258 mild patients and 133 severe patients. Multivariate stepwise regression analyses and discriminant analyses were used to identify and validate powerful predictors. The main outcome was death or invasive ventilation. Three factors, namely higher urea nitrogen (BUN) and IL-6, and lower albumin levels, were the most powerful predictors of mortality, and classified the results (survival vs. death) correctly in 85% of cases (diagnostic accuracy) with a sensitivity of 88% and a specificity 55%. Compared with mild patients, severe patients had lower albumin (ALB), higher alanine aminotransferase (ALT), aspartate aminotransferase (AST) and BUN (all p &lt; 0.001). COVID-19 patients, especially severe patients, have damage to liver and kidney function. BUN, IL-6 and albumin are factors predicting mortality while fibrinogen and AST could be independent factors for predicting the severity of COVID-19.