An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

Some Statistical-Estimation Methods for Stochastic Finite-State Transducers

Formal translations constitute a suitable framework for dealing with many problems in pattern recognition and computational linguistics. The application of formal transducers to these areas requires a stochastic extension for dealing with noisy, distorted patterns with high variability. In this paper, some estimation criteria are proposed and developed for the parameter estimation of regular syntax-directed translation schemata. These criteria are: maximum likelihood estimation, minimum conditional entropy estimation and conditional maximum likelihood estimation. The last two criteria were proposed in order to deal with situations when training data is sparse. These criteria take into account the possibility of ambiguity in the translations: i.e., there can be different output strings for a single input string. In this case, the final goal of the stochastic framework is to find the highest probability translation of a given input string. These criteria were tested on a translation task which has a high degree of ambiguity.     

Academic trajectories of newcomer immigrant youth.

Immigration to the United States presents both challenges and opportunities that affect students' academic achievement. Using a 5-year longitudinal, mixed-methods approach, we identified varying academic trajectories of newcomer immigrant students from Central America, China, the Dominican Republic, Haiti, and Mexico. Latent class growth curve analysis revealed that although some newcomer students performed at high or improving levels over time, others showed diminishing performance. Multinomial logistic regressions identified significant group differences in academic trajectories, particularly between the high-achieving youth and the other groups. In keeping with ecological–developmental and stage–environment fit theories, School Characteristics (school segregation rate, school poverty rate, and student perceptions of school violence), Family Characteristics (maternal education, parental employment, and household structure), and Individual Characteristics (academic English proficiency, academic engagement, psychological symptoms, gender, and 2 age-related risk factors, number of school transitions and being overaged for grade placement) were associated with different trajectories of academic performance. A series of case studies triangulate many of the quantitative findings as well as illuminate patterns that were not detected in the quantitative data. Thus, the mixed-methods approach sheds light on the cumulative developmental challenges that immigrant students face as they adjust to their new educational settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Forecasting with unequally spaced data by a functional principal component approach

The Principal Component Regression model of multiple responses is extended to forccast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data.     

Fascial Innervation: A Systematic Review of the Literature

Currently, myofascial pain has become one of the main problems in healthcare systems. Research into its causes and the structures related to it may help to improve its management. Until some years ago, all the studies were focused on muscle alterations, as trigger points, but recently, fasciae are starting to be considered a new, possible source of pain. This systematic review has been conducted for the purpose of analyze the current evidence of the muscular/deep fasciae innervation from a histological and/or immunohistochemical point of view. A literature search published between 2000 and 2021 was made in PubMed and Google Scholar. Search terms included a combination of fascia, innervation, immunohistochemical, and different immunohistochemical markers. Of the 23 total studies included in the review, five studies were performed in rats, four in mice, two in horses, ten in humans, and two in both humans and rats. There were a great variety of immunohistochemical markers used to detect the innervation of the fasciae; the most used were Protein Gene Marker 9.5 (used in twelve studies), Calcitonin Gene-Related Peptide (ten studies), S100 (ten studies), substance P (seven studies), and tyrosine hydroxylase (six studies). Various areas have been studied, with the thoracolumbar fascia being the most observed. Besides, the papers highlighted diversity in the density and type of innervation in the various fasciae, going from free nerve endings to Pacini and Ruffini corpuscles. Finally, it has been observed that the innervation is increased in the pathological fasciae. From this review, it is evident that fasciae are well innerved, their innervation have a particular distribution and precise localization and is composed especially by proprioceptors and nociceptors, the latter being more numerous in pathological situations. This could contribute to a better comprehension and management of pain.     

LncRNA H19 Impairs Chemo and Radiotherapy in Tumorigenesis

Various treatments based on drug administration and radiotherapy have been devoted to preventing, palliating, and defeating cancer, showing high efficiency against the progression of this disease. Recently, in this process, malignant cells have been found which are capable of triggering specific molecular mechanisms against current treatments, with negative consequences in the prognosis of the disease. It is therefore fundamental to understand the underlying mechanisms, including the genes—and their signaling pathway regulators—involved in the process, in order to fight tumor cells. Long non-coding RNAs, H19 in particular, have been revealed as powerful protective factors in various types of cancer. However, they have also evidenced their oncogenic role in multiple carcinomas, enhancing tumor cell proliferation, migration, and invasion. In this review, we analyze the role of lncRNA H19 impairing chemo and radiotherapy in tumorigenesis, including breast cancer, lung adenocarcinoma, glioma, and colorectal carcinoma.     

Technical Validation and Clinical Implications of Ultrasensitive PCR Approaches for EGFR-Thr790Met Mutation Detection in Pretreatment FFPE Samples and in Liquid Biopsies from Non-Small Cell Lung Cancer Patients

In pretreatment tumor samples of EGFR-mutated non-small cell lung cancer (NSCLC) patients, EGFR-Thr790Met mutation has been detected in a variable prevalence by different ultrasensitive assays with controversial prognostic value. Furthermore, its detection in liquid biopsy (LB) samples remains challenging, being hampered by the shortage of circulating tumor DNA (ctDNA). Here, we describe the technical validation and clinical implications of a real-time PCR with peptide nucleic acid (PNA-Clamp) and digital droplet PCR (ddPCR) for EGFR-Thr790Met detection in diagnosis FFPE samples and in LB. Limit of blank (LOB) and limit of detection (LOD) were established by analyzing negative and low variant allele frequency (VAF) FFPE and LB specimens. In a cohort of 78 FFPE samples, both techniques showed an overall agreement (OA) of 94.20%. EGFR-Thr790Met was detected in 26.47% of cases and was associated with better progression-free survival (PFS) (16.83 ± 7.76 vs. 11.47 ± 1.83 months; p = 0.047). In LB, ddPCR was implemented in routine diagnostics under UNE-EN ISO 15189:2013 accreditation, increasing the detection rate of 32.43% by conventional methods up to 45.95%. During follow-up, ddPCR detected EGFR-Thr790Met up to 7 months before radiological progression. Extensively validated ultrasensitive assays might decipher the utility of pretreatment EGFR-Thr790Met and improve its detection rate in LB studies, even anticipating radiological progression.     

Systematic Review: Drug Repositioning for Congenital Disorders of Glycosylation (CDG)

Advances in research have boosted therapy development for congenital disorders of glycosylation (CDG), a group of rare genetic disorders affecting protein and lipid glycosylation and glycosylphosphatidylinositol anchor biosynthesis. The (re)use of known drugs for novel medical purposes, known as drug repositioning, is growing for both common and rare disorders. The latest innovation concerns the rational search for repositioned molecules which also benefits from artificial intelligence (AI). Compared to traditional methods, drug repositioning accelerates the overall drug discovery process while saving costs. This is particularly valuable for rare diseases. AI tools have proven their worth in diagnosis, in disease classification and characterization, and ultimately in therapy discovery in rare diseases. The availability of biomarkers and reliable disease models is critical for research and development of new drugs, especially for rare and heterogeneous diseases such as CDG. This work reviews the literature related to repositioned drugs for CDG, discovered by serendipity or through a systemic approach. Recent advances in biomarkers and disease models are also outlined as well as stakeholders’ views on AI for therapy discovery in CDG.     

Copper‐Free Clickable Coatings

The copper‐catalyzed azide–alkyne 1,3‐dipolar cycloaddition (CuAAC) is extensively used for the functionalization of well‐defined polymeric materials. However, the necessity for copper, which is inherently toxic, limits the potential applications of these materials in the area of biology and biomedicine. Therefore, the first entirely copper‐free procedure for the synthesis of clickable coatings for the immobilization of functional molecules is reported. In the first step, azide‐functional coatings are prepared by thermal crosslinking of side‐chain azide‐functional polymers and dialkyne linkers. In a second step, three copper‐free click reactions (i.e., the Staudinger ligation, the dibenzocyclooctyne‐based strain‐promoted azide–alkyne [3+2] cycloaddition, and the methyl‐oxanorbornadiene‐based tandem cycloaddition?retro‐Diels?Alder (crDA) reaction) are used to functionalize the azide‐containing surfaces with fluorescent probes, allowing qualitative comparison with the traditional CuAAC.