Miracle-dz/UniFAR-FTUs huggingface dataset:
A Practical Bioinformatics Workflow for Routine Analysis of Bacterial WGS Data

The use of whole-genome sequencing (WGS) for bacterial characterisation has increased substantially in the last decade. Its high throughput and decreasing cost have led to significant changes in outbreak investigations and surveillance of a wide variety of microbial pathogens. Despite the innumerable advantages of WGS, several drawbacks concerning data analysis and management, as well as a general lack of standardisation, hinder its integration in routine use. In this work, a bioinformatics workflow for (Illumina) WGS data is presented for bacterial characterisation including genome annotation, species identification, serotype prediction, antimicrobial resistance prediction, virulence-related genes and plasmid replicon detection, core-genome-based or single nucleotide polymorphism (SNP)-based phylogenetic clustering and sequence typing. Workflow was tested using a collection of 22 in-house sequences of Salmonella enterica isolates belonging to a local outbreak, coupled with a collection of 182 Salmonella genomes publicly available. No errors were reported during the execution period, and all genomes were analysed. The bioinformatics workflow can be tailored to other pathogens of interest and is freely available for academic and non-profit use as an uploadable file to the Galaxy platform.
Bioavailability Assessment of Yarrow Phenolic Compounds Using an In Vitro Digestion/Caco-2 Cell Model: Anti-Inflammatory Activity of Basolateral Fraction

In this study, a combined in vitro digestion/Caco-2 model was performed with the aim to determine the phenolic compounds bioavailability of two yarrow extracts. HPLC-PAD characterisation indicated that the main components in both extracts were 3,5-dicaffeoylquinic acid (DCQA) and luteolin-7-O-glucoside. Analyses after the simulated digestion process revealed that phenolic composition was not affected during the oral phase, whereas gastric and intestinal phases represented critical steps for some individual phenolics, especially intestinal step. The transition from gastric medium to intestinal environment caused an important degradation of 3,5-DCQA (63–67% loss), whereas 3,4-DCQA and 4,5-DCQA increased significantly, suggesting an isomeric transformation within these caffeic acid derivatives. However, an approx. 90% of luteolin-7-O-glucoside was recovered after intestinal step. At the end of Caco-2 absorption experiments, casticin, diosmetin and centaureidin represented the most abundant compounds in the basolateral fraction. Moreover, this fraction presented anti-inflammatory activity since was able to inhibit the secretion of IL-1β and IL-6 pro-inflammatory cytokines. Thus, the presence in the basolateral fraction of flavonoid-aglycones from yarrow, could be related with the observed anti-inflammatory activity from yarrow extract.
Effect of the Pandemic Outbreak on ICU-Associated Infections and Antibiotic Prescription Trends in Non-COVID19 Acute Respiratory Failure Patients

Background: The COVID-19 pandemic had a relevant impact on the organization of intensive care units (ICU) and may have reduced the overall compliance with healthcare-associated infections (HAIs) prevention programs. Invasively ventilated patients are at high risk of ICU-associated infection, but there is little evidence regarding the impact of the pandemic on their occurrence in non-COVID-19 patients. Moreover, little is known of antibiotic prescription trends in the ICU during the first wave of the pandemic. The purpose of this investigation is to assess the incidence, characteristics, and risk factors for ICU-associated HAIs in a population of invasively ventilated patients affected by non-COVID-19 acute respiratory failure (ARF) admitted to the ICU in the first wave of the COVID-19 pandemic, and to evaluate the ICU antimicrobial prescription strategies. Moreover, we compared HAIs and antibiotic use to a cohort of ARF patients admitted to the ICU the year before the pandemic during the same period. Methods: this is a retrospective, single-centered cohort study conducted at S. Anna University Hospital (Ferrara, Italy). We enrolled patients admitted to the ICU for acute respiratory failure requiring invasive mechanical ventilation (MV) between February and April 2020 (intra-pandemic group, IP) and February and April 2019 (before the pandemic group, PP). We excluded patients admitted to the ICU for COVID-19 pneumonia. We recorded patients’ baseline characteristics, ICU-associated procedures and devices. Moreover, we evaluated antimicrobial therapy and classified it as prophylactic, empirical or target therapy, according to the evidence of infection at the time of prescription and to the presence of a positive culture sample. We compared the results of the two groups (PP and IP) to assess differences between the two years. Results: One hundred and twenty-eight patients were screened for inclusion and 83 patients were analyzed, 45 and 38 in the PP and I group, respectively. We found a comparable incidence of HAIs (62.2% vs. 65.8%, p = 0.74) and multidrug-resistant (MDR) isolations (44.4% vs. 36.8% p= 0.48) in the two groups. The year of ICU admission was not independently associated with an increased risk of developing HAIs (OR = 0.35, 95% CI 0.16–1.92, p = 0.55). The approach to antimicrobial therapy was characterized by a significant reduction in total antimicrobial use (21.4 ± 18.7 vs. 11.6 ± 9.4 days, p = 0.003), especially of target therapy, in the IP group. Conclusions: ICU admission for non-COVID-19 ARF during the first wave of the SARS-CoV-2 pandemic was not associated with an increased risk of ICU-associated HAIs. Nevertheless, ICU prescription of antimicrobial therapy changed and significantly decreased during the pandemic.
The Role of Exercise on Cardiometabolic Profile and Body Composition in Youth with Type 1 Diabetes

Exercise has a direct positive effect on glycemic control by promoting insulin secretion from β-pancreatic islet-cells and by increasing skeletal muscle glucose uptake. The reduction in daily insulin needs and the optimization of glycemic control improves the patient’s quality of life, self-esteem, mental wellness, as well as diabetes-related mobility and mortality. The aim of this study was to investigate the effect of physical activity in children and adolescents with type-1 diabetes (T1D) on diabetic control, cardiovascular, and biochemical profiles; hs-CRP; IL6; leptin; and adiponectin levels of the population under study. This is a prospective cross-sectional study that involved 80 participants (36 boys and 44 girls) with T1D, who were aged 6–21 years and who attended the Diabetes and Metabolism Clinic of the 2nd Pediatric Department, University of Athens, “P & A Kyriakou” Children’s Hospital of Athens. Twenty (25%) children were above the 75th percentile regarding total levels of physical activity, while 40 (50%) and 20 (25%) were between the 25th and 75th percentile, as well as below the 25th percentile, respectively. In the group with an intermediate level of exercise, physical activity was negatively associated with the participant’s family situation (traditional, single parent, grandparent, with others, or by himself/herself) (p = 0.013), ferritin (p = 0.031), lipoprotein(a) [Lp(a)] (p = 0.016), and squared leptin levels (p = 0.040). Whereas in the groups with extreme vs. no exercise there was a negative association with the number of daily glucose measurements (p = 0.047). However, in the group with non-vigorous exercise, physical activity was positively associated with high density lipoprotein-c (HDL-c) levels (p = 0.048). The findings of this study are indicative of the beneficial role of exercise on children and adolescents with T1D, which is achieved by primarily improving their cardiometabolic profile through the amelioration of lipid profile [HDL-c, Lp(a)] and leptin levels, as well as by reducing chronic systemic inflammatory response (ferritin) and ultimately decreasing the overall diabetes morbidity.
The Helplessness Dimension of Pain Catastrophizing Mediates the Relation between PTSD Symptoms and Pain Rehabilitation Measures

Background Comorbid chronic pain and post-traumatic stress disorder (PTSD) complicate the treatment of both conditions. Previous research has identified pain catastrophizing as a potentially important variable contributing to the relationship between chronic pain and PTSD. However, little is known regarding how the different dimensions of pain catastrophizing—rumination, magnification, and helplessness—uniquely contribute to the relationship between PTSD symptomatology and measures of pain outcome. Methods 491 treatment seeking participants were admitted to a three-week interdisciplinary pain rehabilitation program between July 2016 and March 2020. The patients completed measures of pain severity, pain interference, pain catastrophizing, depressive symptoms, quality of life (QOL), and PTSD symptoms at pretreatment. Results Parallel mediation analyses were conducted to evaluate the mediating effect of the Pain Catastrophizing Scale subscales on the relationship between PTSD symptomatology and pain-relevant variables. The helplessness subscale accounted for significant unique variance in the relationship between PTSD symptomatology and pain severity (b = 0.010, SE = 0.002, 95% CI: 0.006, 0.014), pain interference (b = 0.004, SE = 0.002, 95% CI: 0.001, 0.008), and mental health QOL (b = −0.117, SE = 0.031, 95% CI: −0.179, −0.059), while the rumination and magnification subscales had no significant influence. Conclusions Pain catastrophizing is a multifaceted construct. These results suggest that the helplessness dimension of pain catastrophizing may be the primary target when treating patients with comorbid chronic pain and PTSD symptoms. This study represents the first to evaluate the influence of the individual dimensions of pain catastrophizing on the relationship between PTSD symptomatology and chronic pain outcome.
Affine Non-negative Representation for Few-Shot Remote Sensing Scene Classification

It is hard to acquire plenty of labeled samples in remote sensing due to the limitations of the imaging environment and the imaging equipment. Recently, few-shot learning, which attempts to identify invisible categories with restricted labeled samples, has shown tremendous potential to address this issue. The Remote Sensing Scene Classification (RSSC) based on fewshot learning generally involves two steps: the pre-training phase and the meta-test phase. Nevertheless, since the sample categories in these two stages are disjoint and only one or a few labeled samples per class are given for training in the meta-testing phase, which causes an “overfitting” problem and thus limits the model’s generalization ability. To overcome the challenge, we propose an Affine Non-negative Representation for Few-Shot RSSC (ANRRSSC). Specifically, we design a representation-based classifier with affine non-negative constraints to prevent “overfitting” by reducing model parameters. Further, the proposed method maps the extracted features into Radial Basis Function (RBF) kernel space to rehabilitate more discriminating features making the data more linearly separable. Extensive experiments on NWPURESISC45 and RSD46-WHU remote sensing image datasets show the merits of the proposed method.
Exploring the Effect of Radiation on the Crystal Structure and Dielectric Response of XLPE

Cables in nuclear power plant (NPP) may be affected by radiation rays during operation, which may lead to insulation failure. Therefore, the changes of cross-linked polyethylene (XLPE) crystal structure and its degradation process under different radiation doses were studied in this paper. First, the cables used in NPPs were irradiated with different doses of $\gamma$-rays, and then the XLPE crystal structure, mechanical properties were obtained by X-ray diffraction test, differential scanning calorimetry test and tensile test. Finally, the interfacial polarization process between the crystal region and the amorphous region after irradiation was analyzed from polarization and depolarization currents. The results showed that with the increase of radiation dose, the crystallinity of XLPE dropped no more than 20% and the crystal size and crystal plane spacing increase about 25%, which indicates that the XLPE crystal region is not completely destroyed by irradiation, but becomes looser. Meanwhile, the elongation at break of 720 kGy sample was less than 50% of the initial value. Irradiation will weaken the connection between the lamellar and make it easier to slip, resulting in a rapid decline in mechanical properties; the interfacial polarization between the crystal region and the amorphous region will become more remarkable due to oxidation and chain scission.
MFCC-based deep convolutional neural network for audio depression recognition

Emotions are a key factor affecting online learning, which has become the new normal due to the COVID-19 pandemic. Identifying students' emotions has important educational implications. Long-term negative emotions can lead to depression, and early identification can help reduce stress and improve learning efficiency. For depressed students in desperate need, current diagnoses are expensive and highly subjective. In response to the above problems, this paper proposes a method for automatic diagnosis of depression with the help of deep convolutional neural networks (Convolutional Neural Network, CNNs). This method inputs the Mel-frequency cepstral coefficients (MFCC) feature maps generated by the preprocessed speech into residual CNNs for training, and adjusts the number of layers and nodes of the network to achieve the highest recognition accuracy. The experimental results are achieved through different residual depths. In ResNet-34, the highest accuracy rate can reach 77%. This will effectively promote the process of emotion recognition research and promote the efficient development of online learning.
Modulation of Somatosensory Evoked Potentials Via Focused Ultrasound Median Nerve Stimulation

Focused ultrasound (FUS) has been shown to modulate peripheral nerve activity, including decreasing perceived pain, with the acoustic radiation force proposed as the mechanism. Pain is subjective, however, and difficult to quantify. Instead, somatosensory evoked potentials (SSEP) from electrical stimulation can be used to quantify sensory signaling from the periphery to the brain and spinal cord. This study investigated the effect and mechanism of FUS median nerve neuromodulation by evaluating nerve displacement, cavitation, and changes in electrically-induced SSEPs. Median nerve FUS sonication at both 1.6 MPa and 2.5 MPa decreased SSEP amplitudes as measured by electroencephalography (EEG). Greater sonication pressure induced greater SSEP amplitude reductions and these reductions corresponded to the magnitude of cumulative median nerve displacement. SSEP reductions were not found with off-target sonication and no in vivo cavitation was found in any subject. Future work will apply this FUS median nerve sonication to modulate pain associated with carpal tunnel syndrome.
Find Out: How Do Your Data Packets Travel?

In today’s communication-centric world, users generate and exchange a massive amount of data. The Internet helps user data to travel from one part of the world to another, via a complex set of network systems. These systems are intelligent, heterogeneous, and non-transparent to users. This paper presents an extensive, trace-driven study of user data traffic covering five years of observations, six large ISPs, 22 different autonomous systems, and a total of 12 countries. This work aims to make users aware of how their data travels in the Internet, as the interests of ISPs majorly influence the data traffic path. Although data traffic should prefer to travel through countries that share land borders, we found that the shortest land distance between the two countries does not impact data path selection.1
Sound Radiation from the Mouth of an Organ Pipe

A double‐ended organ pipe, symmetrical about its midplane, was blown at one of its mouthpieces and radiation patterns were observed in an anechoic chamber. The sharp nulls on the midplane show that the unexcited end is very nearly as strong a source as the blown end. Thus, the blowing jet current, even though chopped by the switching action, does not contribute materially to the radiated sound. This also implies that the jet current does not contribute directly to sustaining oscillations in the pipe.
The Investigation of Conduction Current Characteristics of Segmented PU Elastomers

Polyurethane elastomers (PUs) are a class of multi-phase copolymers commonly composed of polyester or polyether soft segments and urethane hard segments. Microphase separation caused by the thermodynamic incompatibility of the soft segments and the hard segments is believed as the structural basis for many unique properties of PUs, such as shape memory and self-healing. PU is one of the most promising polymers to develop into a new generation of smart self-healing polymer dielectrics due to its shape memory effect and tailor characteristics. Conduction current is not only a basic parameter of dielectrics, but also can reflect many microscopic characteristics of the carrier transport process, which is widely used to investigate the electrical behavior of dielectric materials. The aim of this paper is to report on the conduction current characteristics of segmented PUs. PUs with 50% hard segment content were synthesized with polyether polyol, 4, 4-diphenylmethane diisocyanate (MDI) and 1,4-butanediol (BDO) by a two-step method. The hydrogen bonding interaction of the microphase separation interface is evaluated by the deconvolution of the FT-IR spectra in the carbonyl stretching regions and the amino stretching regions. The charging and discharging behavior of the PU under the applied electrical field strength from 0.2 kV/mm to 1.4 kV/mm is investigated. The results shows that the unique microphase separation structure of PU is a significant reason of interface polarization, leading to an increase in its transient current, which is different from the continuous decay trend of transient currents in most other polymer dielectrics. Meanwhile, when the applied electrical field strength is greater than 1 kV/mm, the measured conduction current is affected by space charge injection, and the discharging current curve shows an increasing peak of heteropolar charge.
On the Performance of TCP in Reconfigurable Data Center Networks

Today’s data centers are hosting various applications under the same roof. The diversity among deployed applications leads to a complex traffic mix in Data Center Networks (DCNs). Reconfigurable Data Center Networks (RD-CNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations and congestion control (CC). This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates? ; This paper focuses on the Transmission Control Protocol (TCP) and presents a measurement study of TCP variants in RDCNs. The quantitative analysis of the measurements shows that migrated flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.
Molecule fluorescence modified by a slit-based nanoantenna with dual gratings

In this study, molecule fluorescence modified by slit-based nanoantennas surrounded with metal gratings was investigated by employing the finite-difference time-domain method. We quantified the relative contribution of excitation and emission gains to the total fluorescence enhancement. The simulation results show that the asymmetric dual-grating (DG) antenna provides an efficient way to control the local excitation enhancement, the collection efficiency, and the quantum efficiency separately for bright emission and beaming light. We also investigated the dependence of fluorescence enhancement on the geometric parameters of the antenna, such as the nano-slit width and number of grooves. The asymmetric DG structure greatly improves the flexibility of the nanostructure design to further optimize the plasmonic enhancement effect and provides a promising route to manipulate single-molecule fluorescence emission.
Flow Characteristics of Fibrous Gas Diffusion Layers Using Machine Learning Methods

The material characteristics of gas diffusion layers are relevant for the efficient operation of polymer electrolyte fuel cells. The current state-of-the-art calculates these using transport simulations based on their micro-structures, either reconstructed or generated by means of stochastic geometry models. Such transport simulations often require high computational resources. To support material characterization using artificial-intelligence-based methods, in this study, a convolutional neural network was developed. It was trained with results from previous transport simulations and validated using five-fold cross-validation. The neural network enables the permeability of paper-type gas diffusion layers to be predicted. A stochastic arrangement of the fibers, four types of binder distributions, and compression of up to 50% are also considered. The binder type and compression level were features inherent to the material but were not the subject of the training. In this regard, they can be seen as features hidden from the training process. Nevertheless, these characteristics were reproduced with the proposed machine learning model. With a trained machine learning model, the prediction of permeability can be performed on a standard computer.
A 4.5 GHz surface excitation solidly mounted microacoustic resonator with 20% coupling

Thanks to improved wafer bonding technologies it is now possible to manufacture wafers with thin single crystal lithium niobate (LN) films with arbitrary orientation. Previous research has focused on interdigitated transducer (IDT) resonators on an LN membrane and cavity. In this work a solidly mounted LN IDT resonator is demonstrated. Hafnium oxide is chosen as a high acoustic impedance reflector material to reduce the parasitic capacitance caused by conductive layers. To ensure the modeling accuracy of hafnium oxide, delay lines have been used to extract a shear stiffness coefficient of 94.4 GPa. To maximize resonator coupling, the LN orientation is chosen to be YX 1 120°• Solidly mounted LN IDT resonators with coupling larger than 20% are demonstrated.