Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

A multi-level approach for hierarchical Ticket Classification

The automatic categorization of support tickets is a fundamental tool for modern businesses. Such requests are most commonly composed of concise textual descriptions that are noisy and filled with technical jargon. In this paper, we test the effectiveness of pre-trained LMs for the classification of issues related to software bugs. First, we test several strategies to produce single, ticket-wise representations starting from their BERT-generated word embeddings. Then, we showcase a simple yet effective way to build a multi-level classifier for the categorization of documents with two hierarchically dependent labels. We experiment on a public bugs dataset and compare our results with standard BERT-based and traditional SVM classifiers. Our findings suggest that both embedding strategies and hierarchical label dependencies considerably impact classification accuracy

Electrodeposited White Bronzes: A Comparison between Zn-Bearing and Zn-Free Coatings

White bronzes are ternary alloys composed of Cu, Zn and Sn, named after their bright whitish color. This class of alloys shares excellent hardness, corrosion and tarnishing resistance, and is commonly adopted in galvanic industrial processes as technological grade coatings to obtain layers with particular aesthetical and/or anticorrosive properties. Despite the widespread employment of white bronzes in fashion and the electronics industry, the recent literature lacks a characterization of these electrodeposited alloys with respect to more common binary (Cu-Sn) white bronzes. In this presentation, a thorough characterization of a commercial ternary Cu-Zn-Sn white bronze, produced by electrodeposition, is reported. Structural, chemical and physical characteristics of the deposited coating were investigated by various techniques (e.g., FIB/SEM, XPS, XRD, EDX, micro-hardness, color and corrosion tests). Results were compared with a similar set of measures obtained from a binary electrodeposited Cu-Sn white bronze (with a high tin content), in order to shed some light on the influence of Zn in the coating properties

Partial T cell defects and expanded CD56bright NK cells in an SCID patient carrying hypomorphic mutation in the IL2RG gene

X-linked severe combined immunodeficiency (X-SCID) caused by full mutation of the IL2RG gene leads to T- B+ NK- phenotype and is usually associated with severe opportunistic infections, diarrhea, and failure to thrive. When IL2RG hypomorphic mutation occurs, diagnosis could be delayed and challenging since only moderate reduction of T and NK cells may be present. Here, we explored phenotypic insights and the impact of the p.R222C hypomorphic mutation (IL2RGR222C ) in distinct cell subsets in an 8-month-old patient with atypical X-SCID. We found reduced CD4+ T cell counts, a decreased frequency of naïve CD4+ and CD8+ T cells, and an expansion of B cells. Ex vivo STAT5 phosphorylation was impaired in CD4+ CD45RO+ T cells, yet compensated by supraphysiological doses of IL-2. Sanger sequencing on purified cell subsets showed a partial reversion of the mutation in total CD3+ cells, specifically in recent thymic emigrants (RTE), effector memory (EM), and CD45RA+ terminally differentiated EM (EMRA) CD4+ T cells. Of note, patient's NK cells had a normal frequency compared to age-matched healthy subjects, but displayed an expansion of CD56bright cells with higher perforin content and cytotoxic potential, associated with accumulation of NK-cell stimulatory cytokines (IL-2, IL-7, IL-15). Overall, this report highlights an alteration in the NK-cell compartment that, together with the high disease-phenotype variability, should be considered in the suspicion of X-SCID with hypomorphic IL2RG mutation

Virological and immunological features of SARS-CoV-2-infected children who develop neutralizing antibodies

As the global COVID-19 pandemic progresses, it is paramount to gain knowledge on adaptive immunity to SARS-CoV-2 in children to define immune correlates of protection upon immunization or infection. We analyzed anti-SARS-CoV-2 antibodies and their neutralizing activity (PRNT) in 66 COVID-19-infected children at 7 (\ub12) days after symptom onset. Individuals with specific humoral responses presented faster virus clearance and lower viral load associated with a reduced in vitro infectivity. We demonstrated that the frequencies of SARS-CoV-2-specific CD4+CD40L+ T cells and Spike-specific B cells were associated with the anti-SARS-CoV-2 antibodies and the magnitude of neutralizing activity. The plasma proteome confirmed the association between cellular and humoral SARS-CoV-2 immunity, and PRNT+ patients show higher viral signal transduction molecules (SLAMF1, CD244, CLEC4G). This work sheds lights on cellular and humoral anti-SARS-CoV-2 responses in children, which may drive future vaccination trial endpoints and quarantine measures policies

Off-label use of combined antiretroviral therapy, analysis of data collected by the Italian Register for HIV-1 infection in paediatrics in a large cohort of children

Background: Early start of highly active antiretroviral therapy (HAART) in perinatally HIV-1 infected children is the optimal strategy to prevent immunological and clinical deterioration. To date, according to EMA, only 35% of antiretroviral drugs are licenced in children  25%. At last check, during the off label regimen, the 80% (40/50) of patients had an undetectable VL, and 90% (45/50) of them displayed CD4 + T lymphocyte percentage > 25%. The most widely used off-label drugs were: dolutegravir/abacavir/lamivudine (16%; 8/50), emtricitbine/tenofovir disoproxil (22%; 11/50), lopinavir/ritonavir (20%; 10/50) and elvitegravir/cobicistat/emtricitabine/ tenofovir alafenamide (10%; 10/50). At logistic regression analysis, detectable VL before starting the current HAART regimen was a risk factor for receiving an off-label therapy (OR: 2.41; 95% CI 1.13-5.19; p = 0.024). Moreover, children < 2 years of age were at increased risk for receiving off-label HAART with respect to older children (OR: 3.24; 95% CI 1063-7.3; p = 0.001). Even if our safety data regarding off-label regimens where poor, no adverse event was reported. Conclusion: The prescription of an off-label HAART regimen in perinatally HIV-1 infected children was common, in particular in children with detectable VL despite previous HAART and in younger children, especially those receiving their first regimen. Our data suggest similar proportions of virological and immunological successes at last check among children receiving off-label or on-label HAART. Larger studies are needed to better clarify efficacy and safety of off-label HAART regimens in children, in order to allow the enlargement of on-label prescription in children