Genomic legacy of migration in endangered caribou.

Wide-ranging animals, including migratory species, are significantly threatened by the effects of habitat fragmentation and habitat loss. In the case of terrestrial mammals, this results in nearly a quarter of species being at risk of extinction. Caribou are one such example of a wide-ranging, migratory, terrestrial, and endangered mammal. In populations of caribou, the proportion of individuals considered as "migrants" can vary dramatically. There is therefore a possibility that, under the condition that migratory behavior is genetically determined, those individuals or populations that are migratory will be further impacted by humans, and this impact could result in the permanent loss of the migratory trait in some populations. However, genetic determination of migration has not previously been studied in an endangered terrestrial mammal. We examined migratory behavior of 139 GPS-collared endangered caribou in western North America and carried out genomic scans for the same individuals. Here we determine a genetic subdivision of caribou into a Northern and a Southern genetic cluster. We also detect >50 SNPs associated with migratory behavior, which are in genes with hypothesized roles in determining migration in other organisms. Furthermore, we determine that propensity to migrate depends upon the proportion of ancestry in individual caribou, and thus on the evolutionary history of its migratory and sedentary subspecies. If, as we report, migratory behavior is influenced by genes, caribou could be further impacted by the loss of the migratory trait in some isolated populations already at low numbers. Our results indicating an ancestral genetic component also suggest that the migratory trait and their associated genetic mutations could not be easily re-established when lost in a population.

Immunotherapy and Cancer: The Multi-Omics Perspective.

Immunotherapies have revolutionized cancer treatment approaches. Because not all patients respond positively to immune therapeutic agents, it represents a challenge for scientists who strive to understand the mechanisms behind such resistance. In-depth exploration of tumor biology, using novel technologies such as omics science, can help decode the role of the tumor immune microenvironment (TIME) in producing a response to the immune blockade strategies. It can also help to identify biomarkers for patient stratification and personalized treatment. This review aims to explore these new models and highlight their possible pivotal role in changing clinical practice.

Mismatch Repair system protein deficiency as a resistance factor for locally advanced rectal adenocarcinoma patients receiving neoadjuvant chemo-radiotherapy.

BACKGROUND: Available data on Mismatch Repair system (MMR) deficiency are conflicting and derived from small studies. Our study aimed to evaluate the therapeutic implications of MMR status in patients with locally advanced rectal cancer (LARC). METHODS: We retrospectively collected data from 318 patients affected by LARC treated in Italy at the Medical Oncology Units of the University Hospital of Cagliari, Istituto Nazionale dei Tumori Milan, and AOU Ospedali Riuniti Ancona. All patients underwent neoadjuvant chemoradiotherapy. The primary objective was major TRG while secondary objectives were pathological complete response, disease-free survival (DFS) and overall survival (OS). RESULTS: One hundred sixty patients (148 pMMR and 12 dMMR) were included in the exploratory cohort and 158 (146 pMMR and 12 dMMR) were included in the validation cohort. A major TRG has been shown in 42.6% and 43.1% patients with pMMR in exploratory and validation cohort, respectively; while no major TRG have been shown in dMMR patients in both cohorts. Exploratory and validation cohorts showed a statistically significant higher mDFS in pMMR patients compared to dMMR: NR vs. 14 months and NR vs. 17 months, respectively. CONCLUSION: Our results indicated an association between dMMR and poor response to preoperative chemoradiotherapy and they represent a hypothesis-generating data for new neoadjuvant strategies.

1D and 2D Field Effect Transistors in Gas Sensing: A Comprehensive Review.

Rapid progress in the synthesis and fundamental understanding of 1D and 2D materials have solicited the incorporation of these nanomaterials into sensor architectures, especially field effect transistors (FETs), for the monitoring of gas and vapor in environmental, food quality, and healthcare applications. Yet, several challenges have remained unaddressed toward the fabrication of 1D and 2D FET gas sensors for real-field applications, which are related to properties, synthesis, and integration of 1D and 2D materials into the transistor architecture. This review paper encompasses the whole assortment of 1D-i.e., metal oxide semiconductors (MOXs), silicon nanowires (SiNWs), carbon nanotubes (CNTs)-and 2D-i.e., graphene, transition metal dichalcogenides (TMD), phosphorene-materials used in FET gas sensors, critically dissecting how the material synthesis, surface functionalization, and transistor fabrication impact on electrical versus sensing properties of these devices. Eventually, pros and cons of 1D and 2D FETs for gas and vapor sensing applications are discussed, pointing out weakness and highlighting future directions.

Vapor-Phase Synthesis of Molecularly Imprinted Polymers on Nanostructured Materials at Room-Temperature.

Molecularly imprinted polymers (MIPs) have recently emerged as robust and versatile artificial receptors. MIP synthesis is carried out in liquid phase and optimized on planar surfaces. Application of MIPs to nanostructured materials is challenging due to diffusion-limited transport of monomers within the nanomaterial recesses, especially when the aspect ratio is >10. Here, the room temperature vapor-phase synthesis of MIPs in nanostructured materials is reported. The vapor phase synthesis leverages a >1000-fold increase in the diffusion coefficient of monomers in vapor phase, compared to liquid phase, to relax diffusion-limited transport and enable the controlled synthesis of MIPs also in nanostructures with high aspect ratio. As proof-of-concept application, pyrrole is used as the functional monomer thanks to its large exploitation in MIP preparation; nanostructured porous silicon oxide (PSiO2 ) is chosen to assess the vapor-phase deposition of PPy-based MIP in nanostructures with aspect ratio >100; human hemoglobin (HHb) is selected as the target molecule for the preparation of a MIP-based PSiO2 optical sensor. High sensitivity and selectivity, low detection limit, high stability and reusability are achieved in label-free optical detection of HHb, also in human plasma and artificial serum. The proposed vapor-phase synthesis of MIPs is immediately transferable to other nanomaterials, transducers, and proteins.

Trapped DNA fragments in marine sponge specimens unveil North Atlantic deep-sea fish diversity.

Sponges pump water to filter feed and for diffusive oxygen uptake. In doing so, trace DNA fragments from a multitude of organisms living around them are trapped in their tissues. Here we show that the environmental DNA retrieved from archived marine sponge specimens can reconstruct the fish communities at the place of sampling and discriminate North Atlantic assemblages according to biogeographic region (from Western Greenland to Svalbard), depth habitat (80-1600 m), and even the level of protection in place. Given the cost associated with ocean biodiversity surveys, we argue that targeted and opportunistic sponge samples - as well as the specimens already stored in museums and other research collections - represent an invaluable trove of biodiversity information that can significantly extend the reach of ocean monitoring.

High taxonomic diversity and miniaturization in benthic communities under persistent natural CO2 disturbances.

Metabarcoding techniques have revolutionized ecological research in recent years, facilitating the differentiation of cryptic species and revealing previously hidden diversity. In the current scenario of climate change and ocean acidification, biodiversity loss is one of the main threats to marine ecosystems. Here, we explored the effects of ocean acidification on marine benthic communities using DNA metabarcoding to assess the diversity of algae and metazoans. Specifically, we examined the natural pH gradient generated by the Fuencaliente CO2 vent system, located near La Palma Island (Canary Islands). High-resolution COI metabarcoding analyses revealed high levels of taxonomic diversity in an acidified natural area for the first time. This high number of species arises from the detection of small and cryptic species that were previously undetectable by other techniques. Such species are apparently tolerant to the acidification levels expected in future oceans. Hence and following our results, future subtropical communities are expected to keep high biodiversity values under an acidification scenario, although they will tend toward overall miniaturization due to the dominance of small algal and invertebrate species, leading to changes in ecosystem functions.

Selection of both habitat and genes in specialized and endangered caribou.

Genetic mechanisms determining habitat selection and specialization of individuals within species have been hypothesized, but not tested at the appropriate individual level in nature. In this work, we analyzed habitat selection for 139 GPS-collared caribou belonging to three declining ecotypes sampled throughout Northwestern Canada. We used Resource Selection Functions (RSFs) comparing resources at used and available locations. We found that the three caribou ecotypes differed in their use of habitat suggesting specialization. On expected grounds, we also found differences in habitat selection between summer and winter, but also, originally, among the individuals within an ecotype. We next obtained Single Nucleotide Polymorphisms (SNPs) for the same caribou individuals, we detected those associated to habitat selection, and then identified genes linked to these SNPs. These genes had functions related in other organisms to habitat and dietary specializations, and climatic adaptations. We therefore suggest that individual variation in habitat selection was based on genotypic variation in the SNPs of individual caribou, indicating that genetic forces underlie habitat and diet selection in the species. We also suggest that the associations between habitat and genes that we detected may lead to lack of resilience in the species, thus contributing to caribou endangerment. Our work emphasizes that similar mechanisms may exist for other specialized, endangered species. This article is protected by copyright. All rights reserved.

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology.

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen-Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.

Emerging treatment evolutions and integrated molecular characteristics of biliary tract cancers.

Biliary tract cancers (BTCs) consist of a group of highly heterogeneous malignancies that are characterized by genomic differences among tumors from different anatomic sites. The current treatment for BTC includes surgery, chemotherapy, target therapy, and immunotherapy. Although surgery remains the primary option for localized disease, representing the only potential curative treatment, a high risk of recurrence cannot be neglected. Chemotherapy has been considered the standard of care for both advanced and metastatic disease and in adjuvant settings. However, drug resistance is a major obstacle associated with chemotherapy. The development of genetic testing technologies, including next-generation sequencing, has opened the door for the identification of drug targets and candidate molecules. A series of preclinical studies has demonstrated the role of gene mutations, abnormal signaling pathways, and immunosuppression in the pathogenesis of BTC, laying the foundation for the application of targeted therapy and immunotherapy. A variety of molecularly targeted agents, including pemigatinib, have shown promising survival benefits in patients with advanced disease. The rapidly evolving role of multimodal therapy represents the subject of this review.

BRCA-mutant pancreatic ductal adenocarcinoma.

Despite continued research, pancreatic ductal adenocarcinoma (PDAC) remains one of the main causes of cancer death. Interest is growing in the role of the tumour suppressors breast cancer 1 (BRCA1) and BRCA2-typically associated with breast and ovarian cancer-in the pathogenesis of PDAC. Indeed, both germline and sporadic mutations in BRCA1/2 have been found to play a role in the development of PDAC. However, data regarding BRCA1/2-mutant PDAC are lacking. In this review, we aim to outline the specific landscape of BRCA-mutant PDAC, focusing on heritability, clinical features, differences between BRCA1 and 2 mutations and between germline and sporadic alterations, as well as established therapeutic strategies and those that are still under evaluation.

Environmental DNA effectively captures functional diversity of coastal fish communities.

Robust assessments of taxonomic and functional diversity are essential components of research programmes aimed at understanding current biodiversity patterns and forecasting trajectories of ecological changes. Yet, evaluating marine biodiversity along its dimensions is challenging and dependent on the power and accuracy of the available data collection methods. Here we combine three traditional survey methodologies (underwater visual census strip transects [UVCt], baited underwater videos [BUV] and small-scale fishery catches [SSFc]), and one novel molecular technique (environmental DNA metabarcoding [eDNA]-12S rRNA and cytochrome oxidase subunit 1 [COI]) to investigate their efficiency and complementarity in assessing fish diversity. We analysed 1,716 multimethod replicates at a basin scale to measure the taxonomic and functional diversity of Mediterranean fish assemblages. Taxonomic identities were investigated at species, genus and family levels. Functional identities were assessed using combinations of morphological, behavioural and trophic traits. We show that: (a) SSFc provided the higher taxonomic diversity estimates followed by eDNA, and then UVCt and BUV; (b) eDNA was the only method able to gather the whole spectrum of considered functional traits, showing the most functionally diversified and least redundant fish assemblages; and (c) the effectiveness of eDNA in describing functional structure reflected its lack of selectivity towards any considered functional trait. Our findings suggest that the reach of eDNA analysis stretches beyond taxon detection efficiency and provides new insights into the potential of metabarcoding in ecological studies.

All is fish that comes to the net: metabarcoding for rapid fisheries catch assessment.

Monitoring marine resource exploitation is a key activity in fisheries science and biodiversity conservation. Since research surveys are time consuming and costly, fishery-dependent data (i.e., derived directly from fishing vessels) are increasingly credited with a key role in expanding the reach of ocean monitoring. Fishing vessels may be seen as widely ranging data-collecting platforms, which could act as a fleet of sentinels for monitoring marine life, in particular exploited stocks. Here, we investigate the possibility of assessing catch composition of single hauls carried out by trawlers by applying DNA metabarcoding to the dense water draining from fishing nets just after the end of hauling operations (hereafter "slush"). We assess the performance of this approach in portraying ß-diversity and examining the quantitative relationship between species abundances in the catch and DNA amount in the slush (read counts generated by amplicon sequencing). We demonstrate that the assemblages identified using DNA in the slush satisfactorily mirror those returned by visual inspection of net content (about 71% of species and 86% of families of fish) and detect a strong relationship between read counts and species abundances in the catch. We therefore argue that this approach could be upscaled to serve as a powerful source of information on the structure of demersal assemblages and the impact of fisheries.

An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics.

In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or load identification are currently related to big data, consisting of structural vibration recordings shaped as a multivariate time series. Any algorithm should therefore allow an effective dimensionality reduction, retaining the informative content of data and inferring correlations within and across the time series. Within this framework, we propose a time series AutoEncoder (AE) employing inception modules and residual learning for the encoding and the decoding parts, and an extremely reduced latent representation specifically tailored to tackle load identification tasks. We discuss the choice of the dimensionality of this latent representation, considering the sources of variability in the recordings and the inverse-forward nature of the AE. To help setting the aforementioned dimensionality, the false nearest neighbor heuristics is also exploited. The reported numerical results, related to shear buildings excited by dynamic loadings, highlight the signal reconstruction capacity of the proposed AE, and the capability to accomplish the load identification task.

Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling.

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

Meta-Fish-Lib: A generalised, dynamic DNA reference library pipeline for metabarcoding of fishes.

The accuracy and reliability of DNA metabarcoding analyses depend on the breadth and quality of the reference libraries that underpin them. However, there are limited options available to obtain and curate the huge volumes of sequence data that are available on public repositories such as NCBI and BOLD. Here, we provide a pipeline to download, clean and annotate mitochondrial DNA sequence data for a given list of fish species. Features of this pipeline include (a) support for multiple metabarcode markers; (b) searches on species synonyms and taxonomic name validation; (c) phylogeny assisted quality control for identification and removal of misannotated sequences; (d) automatically generated coverage reports for each new GenBank release update; and (e) citable, versioned DOIs. As an example we provide a ready-to-use curated reference library for the marine and freshwater fishes of the U.K. To augment this reference library for environmental DNA metabarcoding specifically, we generated 241 new MiFish-12S sequences for 88 U.K. marine species, and make available new primer sets useful for sequencing these. This brings the coverage of common U.K. species for the MiFish-12S fragment to 93%, opening new avenues for scaling up fish metabarcoding across wide spatial gradients. The Meta-Fish-Lib reference library and pipeline is hosted at https://github.com/genner-lab/meta-fish-lib.

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain.

Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditions demand so, and harness the overwhelming amount of patient related data currently at clinicians' disposal. However, these two approaches are rarely synergistically exploited, and tend to be used without integration. In this paper, we follow recent efforts reported in the literature regarding integration of BDI agency with machine learning based Cognitive Services, by proposing an integration architecture, and by validating such architecture in the complex domain of trauma management. In particular, we show that augmentation of a BDI agent, endowed with predefined plans encoding experts' knowledge, with a Cognitive Service, trained on past observed data, can enhance trauma management by reducing over triage episodes.

The Difficult Task of Diagnosing Depression in Elderly People with Cancer: A Systematic Review.

Background: Depression is a common psychiatric problem in the elderly and oncology patients. In elderly people with cancer, depression has a peculiar phenomenology. It has a significant impact on the quality of life. Moreover, it is associated with poor adherence to treatments, increased risk of suicide, and mortality. Nevertheless, the topic of depression in elderly people with cancer remains unexplored. Objective: The main goal of this article is to review the literature from the past 20 years on the relationships between depression, cancer, and aging. Methods: The methods followed the Prisma model for eligibility of studies. The articles in which the keywords "depression", "cancer", " elderly, aging, or geriatric" were present, either in the text or in the abstract, were selected. 8.056 articles, by matching the keywords "depression and elderly and cancer," were identified. Only 532 papers met the eligibility criteria of search limits and selection process. Out of 532 papers, 467 were considered irrelevant, leaving 65 relevant studies. Out of 65 suitable studies, 39 (60.0%) met our quality criteria and were included. Results: The risk factors associated with depression in elderly people with cancer can be divided into 4 groups: 1) tumor-related; 2) anticancer treatment-related; 3) patients-related; 4) number and type of comorbidity. The main obstacles in diagnosing depression in elderly patients with cancer are the overlap of the symptoms of cancer and side effects of treatment with the symptoms of depression but also the different ways of reporting depressive symptoms of elderly people and the different clinical types of depression. There is a lack of data regarding validated scales to assess depression in geriatric patients with cancer. Any mental illness, specifically co-occurring anxiety and depression, increases the risk of diagnosis delay and anticancer treatment adherence. Cancer and the diagnosis of mental disorders prior to cancer diagnosis correlate with an increased risk for suicide. A non-pharmacological therapeutic approach, pharmacological treatment and/or a combination of both can be used to treat elderly patients with cancer, but a detailed analysis of comorbidities and the assessment of polypharmacy is mandatory in order to avoid potential side-effects and interactions between antidepressants and the other drugs taken by the patients. Conclusion: Future research should be conducted with the aim of developing a modified and adapted assessment method for the diagnosis and treatment of depression in elderly people with cancer in order to improve their clinical outcomes and quality of life.

Moldless Printing of Silicone Lenses With Embedded Nanostructured Optical Filters.

Optical lenses are among the oldest technological innovations (3000 years ago) and they have enabled a multitude of applications in healthcare and in our daily lives. The primary function of optical lenses has changed little over time; they serve mainly as a light-collection (e.g. reflected, transmitted, diffracted) element, and the wavelength and/or intensity of the collected light is usually manipulated by coupling with various external optical filter elements or coatings. This generally results in losses associated with multiple interfacial reflections, and increases the complexity of design and construction. In this work we introduce a change in this paradigm, by integrating both light-shaping and image magnification into a single lens element using a moldless procedure that takes advantage of the physical and optical properties of mesoporous silicon (PSi) photonic crystal nanostructures. Casting of a liquid poly(dimethyl) siloxane (PDMS) pre-polymer solution onto a PSi film generates a droplet with contact angle that is readily controlled by the silicon nanostructure, and adhesion of the cured polymer to the PSi photonic crystal allows preparation of lightweight (10 mg) freestanding lenses (4.7 mm focal length) with an embedded optical component (e.g. optical rugate filter, resonant cavity, distributed Bragg reflector). Our fabrication process shows excellent reliability (yield 95%) and low cost and we expect our lens to have implications in a wide range of applications. As a proof-of-concept, using a single monolithic lens/filter element we demonstrate: fluorescence imaging of isolated human cancer cells with rejection of the blue excitation light, through a lens that is self-adhered to a commercial smartphone; shaping the emission spectrum of a white light emitting diode (LED) to tune the color from red through blue; and selection of a narrow wavelength band (bandwidth 5 nm) from a fluorescent molecular probe.

Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach.

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.