Direct coupling analysis and the attention mechanism

Proteins are involved in nearly all cellular functions, encompassing roles in transport, signaling, enzymatic activity, and more. Their functionalities crucially depend on their complex three-dimensional arrangement. For this reason, being able to predict their structure from the amino acid sequence has been and still is a phenomenal computational challenge that the introduction of AlphaFold solved with unprecedented accuracy. However, the inherent complexity of AlphaFold's architectures makes it challenging to understand the rules that ultimately shape the protein's predicted structure. This study investigates a single-layer unsupervised model based on the attention mechanism. More precisely, we explore a Direct Coupling Analysis (DCA) method that mimics the attention mechanism of several popular Transformer architectures, such as AlphaFold itself. The model's parameters, notably fewer than those in standard DCA-based algorithms, can be directly used for extracting structural determinants such as the contact map of the protein family under study. Additionally, the functional form of the energy function of the model enables us to deploy a multi-family learning strategy, allowing us to effectively integrate information across multiple protein families, whereas standard DCA algorithms are typically limited to single protein families. Finally, we implemented a generative version of the model using an autoregressive architecture, capable of efficiently generating new proteins in silico

Diagnostic accuracy of the primary care screener for affective disorder (PC-SAD) in primary care

Background: Depression goes often unrecognised and untreated in non-psychiatric medical settings. Screening has recently gained acceptance as a first step towards improving depression recognition and management. The Primary Care Screener for Affective Disorders (PC-SAD) is a self-administered questionnaire to screen for Major Depressive Disorder (MDD) and Dysthymic Disorder (Dys) which has a sophisticated scoring algorithm that confers several advantages. This study tested its performance against a ‘gold standard’ diagnostic interview in primary care. Methods: A total of 416 adults attending 13 urban general internal medicine primary care practices completed the PC-SAD. Of 409 who returned a valid PC-SAD, all those scoring positive (N=151) and a random sample (N=106) of those scoring negative were selected for a 3-month telephone follow-up assessment including the administration of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-I) by a psychiatrist who was masked to PC-SAD results. Results: Most selected patients (N=212) took part in the follow-up assessment. After adjustment for partial verification bias the sensitivity, specificity, positive and negative predictive value for MDD were 90%, 83%, 51%, and 98%. For Dys, the corresponding figures were 78%, 79%, 8%, and 88%. Conclusions: While some study limitations suggest caution in interpreting our results, this study corroborated the diagnostic validity of the PC-SAD, although the low PPV may limit its usefulness with regard to Dys. Given its good psychometric properties and the short average administration time, the PC-SAD might be the screening instrument of choice in settings where the technology for computer automated scoring is available

Canopy Structure and Forage Production of Lolium rididum Gaudin as influenced by the Frequency of Defoliation

An experiment was conducted in Sardinia to develop an appropriate rotational grazing management regime in spring for an ecotype of annual ryegrass (Lolium rigidum Gaudin). Three intermittent defoliation treatments were compared using sward surface height (10, 15 or 20 cm) to determine time of cutting. Forage dry matter yield, tiller population density, LAI, vertical distribution of plant tissues and other related characteristics were measured. Cutting when sward reached 10 cm resulted In significantly lower yields but a better canopy structure (denser sward, higher percentage of leaves in the bottom layers, higher leaf: sheath ratio) than the other treatments. The results suggest & that the frequently defoliated swards could.be utilized by sheep more efficiently than the others because the bottom layer of the tall swards consisted only of stem and sheath material. This effect could compensate for the lower 101al forage yield of the intensively defoliated sward

RFID technology for blood tracking: An experimental approach for benchmarking different devices

OBJECTIVE: The objective of the paper is to design a testing protocol to measure performances of RFID devices applied to blood supply chain, and to implement an experimental campaign in order to collect performance data. The protocol matches operational conditions in blood supply chain and is particularly tailored to some critical processes, which can benefit from RFID adoption. The paper thus strives at benchmarking performances of inlays, fixed and handheld RFID readers, when deployed in the blood supply chain processes. DESIGN, METHODOLOGY, APPROACH: The adopted testing protocol enables the assessment of performances of RFID devices in processes of the blood supply chain, since it has been developed peculiarly to emulate critical logistics processes. The testing protocol has been designed jointly with hospital personnel involved in every day operations on blood bags and tubes in order to improve processes, in terms of safety and reliability. The testing protocol has been applied to 3 inlays, 2 fixed readers, 1 mobile handheld in 3 logistics processes, all operating according to UHF EPC class 1 gen 2 protocols and ETSI regulations. We measured and compared read rates, accuracies and read times. FINDINGS: The results of the test give a direct insight of performances to be expected from different RFID devices when deployed in a real-world environment. Therefore, it is possible to give answers to how a specific piece of hardware - such as an inlay or a reader - performs, and how it can be effectively used to improve security of patients in healthcare. At the same time, researchers focusing on the business process reengineering of blood supply chain can assess the technical feasibility of the RFID-reengineered logistics processes in order to improve the safety of end users

In Vitro Anti-HIV-1 Reverse Transcriptase and Integrase Properties of Punica granatum L. Leaves, Bark, and Peel Extracts and Their Main Compounds.

In a search for natural compounds with anti-HIV-1 activity, we studied the effect of the ethanolic extract obtained from leaves, bark, and peels of Punica granatum L. for the inhibition of the HIV-1 reverse transcriptase (RT)-associated ribonuclease H (RNase H) and integrase (IN) LEDGFdependent activities. The chemical analyses led to the detection of compounds belonging mainly to the phenolic and flavonoid chemical classes. Ellagic acid, flavones, and triterpenoid molecules were identified in leaves. The bark and peels were characterized by the presence of hydrolyzable tannins, such as punicalins and punicalagins, together with ellagic acid. Among the isolated compounds, the hydrolyzable tannins and ellagic acid showed a very high inhibition (IC50 values ranging from 0.12 to 1.4 microM and 0.065 to 0.09 microM of the RNase H and IN activities, respectively). Of the flavonoids, luteolin and apigenin were found to be able to inhibit RNase H and IN functions (IC50 values in the 3.7–22 microM range), whereas luteolin 7-O-glucoside showed selective activity for HIV-1 IN. In contrast, betulinic acid, ursolic acid, and oleanolic acid were selective for the HIV-1 RNase H activity. Our results strongly support the potential of non-edible P. granatum organs as a valuable source of anti-HIV-1 compounds

Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey

Elemental fingerprinting combined with machine learning techniques as a powerful tool for geographical discrimination of honeys from nearby regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machinelearning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictor

HyperProbe consortium: transforming neuronavigation in glioma surgery with hyperspectral imaging

We present the HyperProbe consortium: a five-years, multinational, EU-funded project started in October, 2022, that aims at developing innovative hyperspectral imaging technologies for clinical translation. HyperProbe works towards providing highly enhanced neuronavigation during glioma resection