Domestic activities at the Linear Pottery site of Elsloo (Netherlands): a look from under the microscoop

Use-wear analysis of a sample of flint tools from the site of Elsloo, situated in the Graetheide cluster (NL), has shed light on the domestic activities carried out within the settlement. It was shown that hide processing predominates. The extent and character of the wear on the hide working implements indi-cates that different processing stages took place, including dehairing and currying. It is suggested that the quality of the end product, the processed hide, must have been very high. Other craft activities are woodworking and the task responsible for ‘polish 23’, possibly flax processing. A large number of sickle blades were found as well, displaying a considerable variation in polish attributes. A possible explana-tion is that different crops were harvested with the same sickle. Spatial analysis of the demonstrated acti-vities has suggested that hide processing was concentrated in one area, possibly supporting the hypothe-sis that in addition to a domestic mode of production, a loose mode of production was practiced as well

Night-time shift work and related stress responses: A study on security guards

Work-related stress can induce a break in homeostasis by placing demands on the body that are met by the activation of two different systems, the hypothalamic\u2013pituitary\u2013adrenal axis and the sympathetic nervous system. Night-shift work alters the body\u2019s exposure to the natural light\u2013 dark schedule and disrupts circadian (daily) rhythms. The greatest effect of night-shift work is the disruption of circadian rhythms. The impact that these disruptions may have on the pathogenesis of many diseases, including cancer, is unknown. This study aims to discover the relationship among three different job activities of security guards and their stress-related responses by evaluating salivary cortisol levels and blood pressure. Methods: Ninety security guards, including night-time workers and night-time and daily-shift workers, were recruited for this study. Each security guard provided two saliva samples before and after three scheduled time points: (i) at 22:00, (ii) at 06:30, and (iii) at 14:00. Results: The results of the study showed a significant alteration in cortisol levels. Night-time shift cortisol levels significantly increased before and after the work shifts. A physiological prevalence of the vagal tone on the cardiocirculatory activity was found during night-shift work. Conclusions: This study indicates that cortisol levels and blood pressure are sensitive markers of biological responses to severe work stress. Shift-change consequences may occur at the end of the night shift when there is a significant increase in the cortisol level and a significant variation in cardiovascular parameters

Sars-cov-2 and the risk assessment document in italian work; specific or generic risk even if aggravated?

In December 2019, several cases of atypical pneumonia were detected in Wuhan city, Hubei province, inland China. The initial outbreak was of considerable size first in China subsequently spread to the rest of the world. Immediately after the epidemic (which according to the World Health Organization had risen to pandemic status), the problem of whether or not to update the occupational risk assessment arose, also considering how the biological risk from SARS CoV-2 should be understood: specific or generic. To this end, we conducted a literature review to identify national health legislation and policies, examining how Italy has addressed the COVID-19 emergency in occupational health planning, in order to develop considerations on the need to update the Risk Assessment Document following the pandemic status. The data that emerged from the review of current legislation allowed us to conclude that the risk from SARS-CoV-2 is in most work activities to be understood as a generic or aggravated generic risk, requiring the employer to apply and control the preventive measures suggested by health authorities to contain the spread of the virus

Neoadjuvant therapy for breast cancer

Objective: To evaluate the frequency of neoadjuvant therapy (NT) in women with stage I–III breast cancer in Italy and whether it is influenced by biological characteristics, screening history, and geographic area. Methods: Data from the High Resolution Study conducted in 7 Italian cancer registries were used; they are a representative sample of incident cancers in the study period (2009–2013). Included were 3546 women aged <85 years (groups <50, 50–69, 70–64, and 75+) with stage I–III breast cancer at diagnosis who underwent surgery. Women were classified as receiving NT if they received chemotherapy, target therapy, and/or hormone therapy before the first surgical treatment. Logistic models were built to test the association with biological and contextual variables. Results: Only 8.2% of women (290 cases) underwent NT; the treatment decreases with increasing age (14.5% in age <50 and 2.2% in age 75+), is more frequent in women with negative receptors (14.8%), HER2-positive (15.7%), and triple-negative (15.6%). The multivariable analysis showed the probability of receiving NT is higher in stage III (odds ratio [OR] 3.83; 95% confidence interval [CI] 2.83–5.18), luminal B (OR 1.87; 95% CI 1.27–2.76), triple-negatives (OR 1.88; 95% CI 1.15–3.08), and in symptomatic cancers (OR 1.98; 95% CI 1.13–3.48). Use of NT varied among geographic areas: Reggio Emilia had the highest rates (OR 2.29; 95% CI 1.37–3.82) while Palermo had the lowest (OR 0.41; 95% CI 0.24–0.68). Conclusions: The use of NT in Italy is limited and variable. There are no signs of greater use in hospitals with more advanced care

Age-dependent association of white matter abnormality with cognition after TIA or minor stroke

ObjectiveTo investigate if the association between MRI-detectable white matter hyperintensity (WMH) and cognitive status reported in previous studies persists at older ages (>80 years), when some white matter abnormality is almost universally reported in clinical practice.MethodsConsecutive eligible patients from a population-based cohort of all TIA/nondisabling stroke (Oxford Vascular Study) underwent multimodal MRI, including fluid-Attenuated inversion recovery and diffusion-weighted imaging, allowing automated measurement of WMH volume, mean diffusivity (MD), and fractional anisotropy (FA) in normal-Appearing white matter using FSL tools. These measures were related to cognitive status (Montreal Cognitive Assessment) at age 6480 vs >80 years.ResultsOf 566 patients (mean [range] age 66.7 [20-102] years), 107 were aged >80 years. WMH volumes and MD/FA were strongly associated with cognitive status in patients aged 6480 years (all p < 0.001 for WMH, MD, and FA) but not in patients aged >80 years (not significant for WMH, MD, and FA), with age interactions for WMH volume (pinteraction = 0.016) and MD (pinteraction = 0.037). Voxel-wise analyses also showed that lower Montreal Cognitive Assessment scores were associated with frontal WMH in patients 6480 years, but not >80 years.ConclusionMRI markers of white matter damage are strongly related to cognition in patients with TIA/minor stroke at younger ages, but not at age >80 years. Clinicians and patients should not overinterpret the significance of these abnormalities at older ages

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less)

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less)