Interpretation and Dynamics of the Lotka–Volterra Model in the Description of a Three-Level Laser

In this work, the Lotka–Volterra equations where applied to laser physics to describe population inversion and the number of emitted photons. Given that predation and stimulated emissions are analogous processes, two rate equations where obtained by finding suitable parameter transformations for a three-level laser. This resulted in a set of differential equations which are isomorphic to several laser models under accurate parameter identification. Furthermore, the steady state provided two critical points: one where light amplification stops and another where continuous-wave operation is achieved. Lyapunov’s first method of stability yielded the conditions for the convergence to the continuous-wave point, whereas a Lyapunov potential provided its stability regions. Finally, the Q-Switching technique was modeled by introducing a periodic variation of the quality Q of the cavity. This resulted in the transformation of the asymptotically stable fixed point into a limit cycle in the phase space.

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.

SUIHTER : a new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy

The COVID-19 epidemic is the latest in a long list of pandemics that have affected humankind in the last century. In this paper, we propose a novel mathematical epidemiological model named SUIHTER from the names of the seven compartments that it comprises: susceptible uninfected individuals ( S ), undetected (both asymptomatic and symptomatic) infected ( U ), isolated infected ( I ), hospitalized ( H ), threatened ( T ), extinct ( E ) and recovered ( R ). A suitable parameter calibration that is based on the combined use of the least-squares method and the Markov chain Monte Carlo method is proposed with the aim of reproducing the past history of the epidemic in Italy, which surfaced in late February and is still ongoing to date, and of validating SUIHTER in terms of its predicting capabilities. A distinctive feature of the new model is that it allows a one-to-one calibration strategy between the model compartments and the data that are made available daily by the Italian Civil Protection Department. The new model is then applied to the analysis of the Italian epidemic with emphasis on the second outbreak, which emerged in autumn 2020. In particular, we show that the epidemiological model SUIHTER can be suitably used in a predictive manner to perform scenario analysis at a national level.

Simple Ways to Estimate Meningioma Volume: Can ABC- and SH-Derived Methods Be Used in Clinical Practice Reliably?

Background. There is a clinical demand for rapid estimation of meningioma volumes. Our objective was to assess the accuracy of three ABC-derived and three SH-derived formula methods on volume estimation of meningiomas. Methods. The study group comprised 678 patients treated at our department for histopathologically proven intracranial meningiomas. For each patient, tumor volumes were independently measured using six formula methods as well as planimetry. Maximum tumor diameter and ellipsoidity were also recorded. Volumes were compared using descriptive statistics, correlation analysis, and consistency analysis. Results. Among all methods assessed, 2/3SH and 1/2ABC outperformed the others. No significant differences were found between volumes obtained by the two methods and those of planimetry ( p > 0.05 ). Spearman rank-correlation coefficients (rs) were 0.99 for both methods ( p < 0.01 ), and ICC were 0.99 and 0.98, respectively. In Bland-Altman plot, most data points lay inside the limit of agreement. Overall, 2/3SH overestimated tumor volumes by 1.29%, and estimation errors in 93.66% cases were within 20%; 1/2ABC overestimated tumor volumes by 5.36%, and estimation errors in 93.51% cases were within 30%. The performance of 2/3SH and 1/2ABC in small-volume meningiomas was slightly worse, especially for 1/2ABC. Correlations between ellipsoidity and percentage errors of 2/3SH and 1/2ABC were weak (rs = −0.06 and −0.24, respectively). Despite a significant correlation between maximum tumor diameter and planimetric volume (rs = −0.96), volumes could vary significantly for a given diameter. Conclusions. Formula methods 2/3SH and 1/2ABC can estimate meningioma volumes with decent accuracy. Compared with the 1/2ABC method, the 2/3SH method showed slightly better performance, especially in small-volume meningiomas. Ellipsoidity is not a suitable parameter to predict estimation error, and maximum tumor diameter is not a reliable surrogate for actual meningioma volume.

The contribution of resident physicians to hospital productivity

Resident physicians play a double role in hospital activity. They participate in medical practices and thus, on the one hand, they should be considered as an input. Also, they are medical staff in training and, on the other hand, must be considered as an output. The net effect on hospital activities should therefore be empirically determined. Additionally, when considering their role as active physicians, a natural hypothesis is that resident physicians are not more productive than senior ones. This is a property that standard logarithmic production functions (including Cobb–Douglas and Translog functional forms) cannot verify for the whole technology set. Our main contribution is the development of a Translog modification, which implies the definition of the input “doctors” as a weighted sum of senior and resident physicians, where the weights are estimated from the empirical application. This modification of the standard Translog is able, under suitable parameter restrictions, to verify our main hypothesis across the whole technology set while determining if the net effect of resident physicians in hospitals’ production should be associated to an output or to an input. We estimate the resulting output distance function frontier with a sample of Spanish hospitals. Our findings show that the overall contribution of resident physicians to hospitals’ production allows considering them as an input in most cases. In particular, their average productivity is around 37% of that corresponding to senior physicians.

Detection of Carrageenan in Cheese Using Lectin Histochemistry

Carrageenan is a substance widely used as an additive in the food industry. Among other things, it is often added to processed cheese, where it has a positive effect on texture. Processing of such cheese involves grinding, melting and emulsifying the cheese. There is currently no official method by which carrageenan can be detected in foodstuffs, but there are several studies describing its negative health impact on consumers. Lectin histochemistry is a method that is used mainly in medical fields, but it has great potential to be used in food analysis as well. It has been demonstrated that lectin histochemistry can be used to detect carrageenan in processed cheese by Human Inspection and Computer-Assisted Analysis (CIE L*a*b*). The limit of detection (LoD) was established at 100 mg kg−1 for Human Inspection and 43.64 for CIE L*a*b*. The CIE L*a*b* results indicate that Computer-Assisted Analysis may be an appropriate alternative to Human Inspection. The most suitable parameter for Computer-Assisted Analysis was the b* parameter in the CIE L*a*b* color space.

SERUM MYELOPEROXIDASE, MALONDIALDEHYDE, ALPHA-SYNUCLEIN LEVELS IN PATIENTS WITH EPILEPSY

Background: Processes such as neurodegeneration, hypoxia, blood brain barrier dysfunction and oxidative changes are effective for epileptogenesis.There is no non-invasive biomarker that can be used in the follow-up of patients with epilepsy, which is a neurodegenerative disease. Objective: In our study, it was aimed to investigate the relationship between inflammatory, oxidative, neurodegenerative processes, and antiepileptic use in patients with epilepsy. Methods: The groups were formed from the patients who were followed up in the epilepsy outpatient clinic between April 2019-June 2019, and the age-gender-matched control group.The study included 30 patients and 30 healthy volunteers. Venous serum samples were collected from groups to study myeloperoxidase, malondialdehyde and alpha-synuclein. Results: The levels of myeloperoxidase and malondialdehyde were higher in the control group and this difference was statistically significant (p=0.003, p<0.001). The level of α-syn was higher in the epilepsy group and there was no statistically significant difference between the two groups (p=0.52). There was a positive correlation between the α-syn level and disease duration and as the disease duration increased, the level of α-syn increased (r=0.379, p=0.03). Conclusion: Although the α-syn level increases with the duration of the disease in epilepsy patients, it is not a suitable parameter for use as a biomarker in the follow-up.

Biorefining: the role of endoglucanases in refining of cellulose fibers

With an annual production of more than 400 million tons, paper is the main product of the largest biorefinery process industrially implemented. Enzymes have been used for pulp refining to dramatically reduce energy consumption. However, exact mechanisms related to the individual enzymes are hardly understood. Yet, this knowledge would be important to predict enzyme performance in industrial processes. Three commercial refining enzyme formulations showed different endoglucanase (1.25 nkat mg−1–13.7 nkat mg−1), β-glucosidase (0.57 nkat mg−1–1.34 nkat mg−1) and xylanase activities (1.78 nkat ml−1–62.1 nkat mg−1) on model substrates. Additionally, distinct amounts of reducing sugars from hardwood sulfate pulp were released. Endoglucases were purified from each formulation by using hydrophobic interaction and anion exchange chromatography and showed molecular weights from 20 to 55 kDa and specific activities ranging between 3.11 and 26.3 nkat mg−1 according to endoglucanase specific derivatized cellopentaose (CellG5). Refining trials of hardwood sulfate pulp were conducted using a PFI laboratory mill and fiber properties such as degree of refining or fiber length and properties of formed sheets like tensile index were monitored. Thereby, enzymes were dosed based on identical endoglucanase activity on CellG5. Enzyme formulations and purified endoglucanases led to an increase of the degree of refining of up to 47.9 [°SR] at 6000 PFI revolutions while the tensile index was improved by up to 76.0 Nm g−1. In summary, refining effects can be primarily attributed to endoglucanases indicating activity on CellG5 being a suitable parameter for enzyme dosing.

Introducing 3D-potting: a novel production process for artificial membrane lungs with superior blood flow design

Currently, artificial-membrane lungs consist of thousands of hollow fiber membranes where blood flows around the fibers and gas flows inside the fibers, achieving diffusive gas exchange. At both ends of the fibers, the interspaces between the hollow fiber membranes and the plastic housing are filled with glue to separate the gas from the blood phase. During a uniaxial centrifugation process, the glue forms the “potting.” The shape of the cured potting is then determined by the centrifugation process, limiting design possibilities and leading to unfavorable stagnation zones associated with blood clotting. In this study, a new multiaxial centrifugation process was developed, expanding the possible shapes of the potting and allowing for completely new module designs with potentially superior blood flow guidance within the potting margins. Two-phase simulations of the process in conceptual artificial lungs were performed to explore the possibilities of a biaxial centrifugation process and determine suitable parameter sets. A corresponding biaxial centrifugation setup was built to prove feasibility and experimentally validate four conceptual designs, resulting in good agreement with the simulations. In summary, this study shows the feasibility of a multiaxial centrifugation process allowing greater variety in potting shapes, eliminating inefficient stagnation zones and more favorable blood flow conditions in artificial lungs. Graphic abstract