Parameter estimation and model selection for stochastic differential equations for biological growth

In this paper, we consider stochastic versions of three classical growth models given by ordinary differential equations (ODEs). Indeed we use stochastic versions of Von Bertalanffy, Gompertz, and Logistic differential equations as models. We assume that each stochastic differential equation (SDE) has some crucial parameters in the drift to be estimated and we use the Maximum Likelihood Estimator (MLE) to estimate them. For estimating the diffusion parameter, we use the MLE for two cases and the quadratic variation of the data for one of the SDEs. We apply the Akaike information criterion (AIC) to choose the best model for the simulated data. We consider that the AIC is a function of the drift parameter. We present a simulation study to validate our selection method. The proposed methodology could be applied to datasets with continuous and discrete observations, but also with highly sparse data. Indeed, we can use this method even in the extreme case where we have observed only one point for each path, under the condition that we observed a sufficient number of trajectories. For the last two cases, the data can be viewed as incomplete observations of a model with a tractable likelihood function; then, we propose a version of the Expectation Maximization (EM) algorithm to estimate these parameters. This type of datasets typically appears in fishery, for instance

Disruption of ph dynamics suppresses proliferation and potentiates doxorubicin cytotoxicity in breast cancer cells

The reverse pH gradient is a major feature associated with cancer cell reprogrammed metabolism. This phenotype is supported by increased activity of pH regulators like ATPases, carbonic anhydrases (CAs), monocarboxylate transporters (MCTs) and sodium–proton exchangers (NHEs) that induce an acidic tumor microenvironment, responsible for the cancer acid-resistant phenotype. In this work, we analyzed the expression of these pH regulators and explored their inhibition in breast cancer cells as a strategy to enhance the sensitivity to chemotherapy. Expression of the different pH regulators was evaluated by immunofluorescence and Western blot in two breast cancer cell lines (MDA-MB-231 and MCF-7) and by immunohistochemistry in human breast cancer tissues. Cell viability, migration and invasion were evaluated upon exposure to the pH regulator inhibitors (PRIs) concanamycin-A, cariporide, acetazolamide and cyano-4-hydroxycinnamate. Additionally, PRIs were combined with doxorubicin to analyze the effect of cell pH dynamic disruption on doxorubicin sensitivity. Both cancer cell lines expressed all pH regulators, except for MCT1 and CAXII, only expressed in MCF-7 cells. There was higher plasma membrane expression of the pH regulators in human breast cancer tissues than in normal breast epithelium. Additionally, pH regulator expression was significantly associated with different molecular subtypes of breast cancer. pH regulator inhibition decreased cancer cell aggressiveness, with a higher effect in MDA-MB-231. A synergistic inhibitory effect was observed when PRIs were combined with doxorubicin in the breast cancer cell line viability. Our results support proton dynamic disruption as a breast cancer antitumor strategy and the use of PRIs to boost the activity of conventional therapy.This research was funded by National funds, through the Foundation for Science and Technology (FCT) - project UIDB/50026/2020 and UIDP/50026/2020; and by the projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-000023, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by an internal CESPU project MetabRes_CESPU_2017. DT-V received a fellowship from FCT (ref. SFRH/BD/103025/2014)

Scanning-gate microscopy of semiconductor nanostructures: an overview

This paper presents an overview of scanning-gate microscopy applied to the imaging of electron transport through buried semiconductor nanostructures. After a brief description of the technique and of its possible artifacts, we give a summary of some of its most instructive achievements found in the literature and we present an updated review of our own research. It focuses on the imaging of GaInAs-based quantum rings both in the low magnetic field Aharonov-Bohm regime and in the high-field quantum Hall regime. In all of the given examples, we emphasize how a local-probe approach is able to shed new, or complementary, light on transport phenomena which are usually studied by means of macroscopic conductance measurements.Comment: Invited talk by SH at 39th "Jaszowiec" International School and Conference on the Physics of Semiconductors, Krynica-Zdroj, Poland, June 201

Efficacy of dignity therapy for depression and anxiety in terminally-ill patients: early results of a randomized controlled trial

Objective: Dignity therapy (DT) is a short-term psychotherapy developed for patients living with a life-limiting illness. Our aim was to determine the influence of DT on symptoms of depression and anxiety in people with a life-threatening disease with high level of distress, referred to an inpatient palliative care unit. Method: This was an open-label randomized controlled trial. Sixty terminally ill patients were randomly assigned to one of two groups: intervention group (DT+ standard palliative care [SPC]) or control group (SPC alone). The main outcomes were symptoms of depression and anxiety, measured with the Hospital Anxiety and Depression Scale, assessed at baseline, day 4, day 15, and day 30 of follow-up. Results: Of the 60 participants, 29 were randomized to DT and 31 to SPC. Baseline characteristics were similar between the two groups. DT was associated with a significant decrease in depressive symptoms at day 4 and day 15 (mean = −4.46, 95% CI, −6.91–2.02, p = 0.001; mean= −3.96, 95% CI, −7.33 to −0.61; p = 0.022, respectively), but not at day 30 (mean = −3.33, 95% CI, −7.32–0.65, p = 0.097). DT was also associated with a significant decrease in anxiety symptoms at each follow-up (mean= −3.96, 95% CI, −6.66 to −1.25, p = 0.005; mean= −6.19, 95% CI, −10.49 to −1.88, p = 0.006; mean = −5.07, 95% CI, −10.22 to −0.09, p = 0.054, respectively). Significance of results: DT appears to have a short-term beneficial effect on the depression and anxiety symptoms that often accompany patients at the end of their lives. Future research with larger samples compared with other treatments is needed to better understand the potential benefits of this psychotherapy

Transport inefficiency in branched-out mesoscopic networks: An analog of the Braess paradox

We present evidence for a counter-intuitive behavior of semiconductor mesoscopic networks that is the analog of the Braess paradox encountered in classical networks. A numerical simulation of quantum transport in a two-branch mesoscopic network reveals that adding a third branch can paradoxically induce transport inefficiency that manifests itself in a sizable conductance drop of the network. A scanning-probe experiment using a biased tip to modulate the transmission of one branch in the network reveals the occurrence of this paradox by mapping the conductance variation as a function of the tip voltage and position.Comment: 2nd version with minor stylistic corrections. To appear in Phys. Rev. Lett.: Editorially approved for publication 6 January 201

Increased expression of monocarboxylate transporters 1, 2, and 4 in colorectal carcinomas

Tumour cells are known to be highly glycolytic, thus producing high amounts of lactic acid. Monocarboxylate transporters (MCTs), by promoting the efflux of the accumulating acids, constitute one of the most important mechanisms in the maintenance of tumour intracellular pH. Since data concerning MCT expression in colorectal carcinomas (CRC) are scarce and controversial, the present study aimed to assess the expressions of MCT1, 2, and 4 in a well characterized series of CRC and assess their role in CRC carcinogenesis. CRC samples (126 cases) were analyzed for MCT1, MCT2, and MCT4 immunoexpression and findings correlated with clinico-pathological parameters. Expression of all MCT isoforms in tumour cells was significantly increased when compared to adjacent normal epithelium. Remarkably, there was a significant gain of membrane expression for MCT1 and MCT4 and loss of plasma membrane expression for MCT2 in tumour cells. Plasma membrane expression of MCT1 was directly related to the presence of vascular invasion. This is the larger study on MCT expression in CRC and evaluates for the first time its clinico-pathological significance. The increased expression of these transporters suggests an important role in CRC, which might justify their use, especially MCT1 and MCT4, as targets in CRC drug therapy

Robust semi-parametric inference for two-stage production models: A beta regression approach

The data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal.ANCD -Agenția Națională pentru Cercetare și Dezvoltare(1200525

Estrogen protection in Parkinson´s disease – a GDNF role?

Estrogen protection in Parkinson´s disease – a GDNF role