Are family ties an opportunity or an obstacle for women entrepreneurs? Some empirical evidence from Italy?

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Virtual Relations and Value Creation for female enterprises during the Covid-19 pandemic era

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Wood pre-treatments a short review

Pre-treatments to improve wood durability, as well as their impact on Life Cycle Assessment (LCA) are important aspects to take into account in the first phase of a project. The objective of this paper is to study the effects pre-treatments have on durability of wood when compared with the possibility of reusing wood components over their entire life cycle. Studies on how artificial/natural processes change the mechanical, physical and chemical properties of wood are undertaken in different scientific fields. The biology of wood studies the chemical and natural processes that affect it. The study is often focussed on the biochemistry and molecular composition of wood, and analyses how the various chemical processes (both natural and otherwise) affect the characteristics of the material, determining the decay of performance and the deterioration of the components. Material engineering studies the mechanical modifications of parameters (e.g. modulus of elasticity, modulus of rupture) without considering the implication that pre-treatment choices have in the building process. In this article we provide a review of the most important pre-treatments for improving wood properties (e.g. strength, water absorption, etc.) compared with processing times and LCA parameters. In particular, we started with a literature review to gather an overall understanding about the different processes that can be applied to improve material durability, and propose a preliminary pre-treatment classification. Durability can be defined as the material’s capability to ensure adequate values of performance and functional levels over its entire lifetime. As known, wood has undesirable reactions to atmospheric agents if it’s not sufficiently protected. There are different pre-treatments that can change its physical, chemical, or mechanical properties. These processes can be applied alone or in combination [1] and are subdivided into: Thermal pre-treatments Chemical pre-treatments Mechanical pre-treatments Thermal pre-treatments use high temperature steam (up to 230 °C) or hot water (up to 180 °C) [5]. Laboratory tests show that these processes increase the dimensional stability of wood and resistance to moisture variations. In particular, these results were widely observed in wood panels (OSB, MDF, WPC). It is also observed that these wood preservation techniques prevent or at least reduce the possibility of attacks by biological agents such as insects and fungi [3]. A drawback of this process is a decrease in the mechanical properties of wood. Different laboratory tests have shown how both the modulus of elasticity (MOE) and modulus of rupture (MOR) steadily decrease after the thermal pre-treatment. Chemical pre-treatments can be applied on the external layer of the material, or by means of long lasting impregnation of the components. Chemical treatments are usually administered on wood to prevent performance reduction, improve water resistance, reduce the effects of ultraviolet radiation, or decrease flammability [6][7].The property of the material to absorb chemical treatments is related to material’s hydrophilicity. Treated wood must be non-toxic and recyclable at the end of its service-life [3]and this property is not always guaranteed with all chemical treatments. Mechanical pre-treatments are used to reduce the internal moisture. Different tests were performed in China and Japan, to investigate the relation between compression rate and moisture content. There is no clear evidence of how the compression ratio, compression direction, and compression speed affect the decrease of moisture content and mechanical properties. The speed of compression should influence the efficiency of processing, and the final moisture content [8].The tests show that the material undergoes no substantial decrease of both MOE and MOR parameters. In conclusion, besides providing indications about the different pre-treatment methods, this paper will also assess their impact on the environment. In this study we want to propose an innovative approach to understand both the advantages and disadvantages of the described treatment procedures, thus providing a novel contribution in the field of construction and wood design

Artificial intelligence and new business models in agriculture: a structured literature review and future research agenda

Purpose Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda. Design/methodology/approach A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework. Findings Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners. Originality/value The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified

The efficacy and predictability of maxillary first molar derotation with invisalign: a prospective clinical study in growing subjects

To analyze the derotation of maxillary mesio-rotated first permanent molars in subjects with Class II edge-to-edge dental malocclusion in mixed dentition treated with Invisalign Clear Aligners (CA). In total, 36 patients (16 males, 20 females, 9.9 ± 1.9 years) treated with CA are enrolled from the Department of Orthodontics. Inclusion criteria are the following: Caucasian ancestry, mixed dentition, molar Class II edge-to-edge, no tooth/craniofacial anomalies, no caries/periodontal diseases. Pre-treatment (T1) and post-treatment (T2) digital casts, and final ClinCheck representations (T2ClinCheck) are acquired. The Henry’s angle (HA) is used to assess maxillary first molars rotation. The molars with an HA > 11° are taken (53 teeth). Five measurements are performed at T1, T2, and T2ClinCheck: Henry’s angle (HA), mesiobuccal-expansion (MBE), distobuccal-expansion (DBE), mesiobuccal-sagittal (MBS), and distobuccal-sagittal (DBS). A paired t-test was used to compare T2-T1 and T2ClinCheck-T2. The T2-T1 shows a distal-rotation (difference −6.3°) with an expansion of 2.2 mm for MBE and 1.5 mm for DBE. At T2, the mesiobuccal cusps show a distal movement of 1.0 mm and the distobuccal cusps of 0.9 mm. The HA’s T2ClinCheck-T2 difference is −4.2°. In the sagittal plane, the difference is 0.9 mm for the MBS and 0.7 mm for the DBS. The expansion showed the highest predictability (60% HA, 52.6% MBS, and 56.25% DBS). The CA effectively produces an arch expansion and upper molars’ distal rotation. Upper molar derotation provides a 1 mm of gain in arch perimeter and occlusal improvement

Morphometric covariation between palatal shape and skeletal pattern in Class II growing subjects

Objectives To evaluate the patterns of covariation between palatal and craniofacial morphology in Class II subjects in the early mixed dentition by means of geometric morphometrics. Methods A cross-sectional sample of 85 Class II subjects (44 females, 41 males; mean age 8.7 years ± 0.8) was collected retrospectively according to the following inclusion criteria: European ancestry (white), Class II skeletal relationship, Class II division 1 dental relationship, early mixed dentition, and prepubertal skeletal maturation. Pre-treatment digital 3D maxillary dental casts and lateral cephalograms were available. Landmarks and semilandmarks were digitized (239 on the palate and 121 on the cephalogram) and geometric morphometric methods (GMM) were applied. Procrustes analysis and principal component analysis (PCA) were performed to reveal the main patterns of palatal shape and craniofacial skeletal shape variation. Two-block partial least squares analysis (PLS) assessed patterns of covariation between palatal morphology and craniofacial morphology. Results For the morphology of the palate, the first principal component (PC1) described variation in all three dimensions. For the morphology of the craniofacial complex, PC1 showed shape variation mainly in the vertical direction. Palatal shape and craniofacial shape covaried significantly (RV coefficient: 0.199). PLS1 accounted for more than 64 per cent of total covariation and related divergence of the craniofacial complex to palatal height and width. The more a Class II subject tended towards high-angle divergence, the narrower and higher was the palate. Conclusions Class II high-angle patients tended to have narrower and higher palates, while Class II low-angle patients were related to wider and more shallow palates

Clinical characteristics and outcome of patients with autoimmune hemolytic anemia (AIHA) uniformly defined as primary by a diagnostic work-up

Primary autoimmune hemolytic anemia (P-AIHA) is a relatively uncommon and hetereogeneous disease characterized by the destruction of red blood cells due to anti-erythrocyte autoantibodies (AeAbs) in the absence of an associated disease [1–3]. Secondary AHIA is frequently associated with lymphoproliferative diseases (LD) in particular, chronic lymphocytic leukemia, aggressive or indolent lymphomas, autoimmune disorders, malignancies other than lymphoid, and infections [1,2,4]. On the hypothetical assumption that in a significant proportion of cases defined as P-AIHA the clinical heterogeneity could be due to an ignored associated disease, we retrospectively analyzed the clinical characteristics and outcome of patients with a diagnosis of P-AIHA based on a diagnostic work-up aimed at excluding or identifying an associated disease. ..

The Addition of Venetoclax to Induction Chemotherapy in No Low-Risk AML Patients: A Propensity Score-Matched Analysis of the Gimema AML1718 and AML1310 Trials

Venetoclax combined with intensive chemotherapy proved to be safe with promising activity in fit patients with no-low-risk newly diagnosed acute myeloid leukemia (AML), as demonstrated also by an intermediate analysis of the GIMEMA AML1718 trial (NCT03455504). The latter trial, still ongoing, is based on the administration of venetoclax-FLAI to intermediate/high-risk ELN2017 AML and produced a complete remission (CR) rate of 84%, a minimal residual disease (MRD)-negativity rate of 74% and a 12-month Overall Survival (OS) and disease-free survival (DFS) of 75.7% (95%CI: 64.1%, 89.5%) and 80.7% (95%CI: 67.9%, 95.9%), respectively. In order to evaluate the actual advantage of the addition of venetoclax to chemotherapy, the GIMEMA AML1718 was matched to AML1310, which entailed a "3+7"-like induction and a risk-adapted, MRD-directed post-remission transplant allocation (NCT01452646, Venditti et al - Blood 2019). To generate a reliable comparison, AML1718 and AML1310 were matched by using a propensity score and then compared in terms of CR achievement, MRD-negativity and survival outcomes. Patient-level data from GIMEMA AML1718 (n=57) and AML1310 (N=445) with ELN2017 risk classification available were used to conduct a propensity score matching analysis, widely used for reducing the effects of confounding when estimating the effects of treatment on outcomes. Conditional on the propensity score, the distribution of measured variables is expected to be the same in treated (i.e. AML1718) and control (i.e. AML1310) subjects. In the present propensity score model, we included the following variables: age at diagnosis, gender, ELN2017 risk classification and transplant. Different methods for matching were attempted, including 1:1 nearest neighbor, full-matching, optimal matching (1:2, 1:3 and 1:4) and 1:2 genetic matching. The methods employed for assessing balancing were: i) Standardized Mean Difference - Love plot, ii) Empirical cumulative density function, iii) Variance ratio, iv) Empirical QQ-plot. Weights were calculated with probit or logit regression models according to the propensity score method used. Weights obtained from full-matching were used to adjust outcomes (CR, MRD negativity and survival outcomes). No patients were dropped in the full-matching process. A standardized bias score less than 0.25 was used as a criterion for adequate balancing. We used balance tables and Love plots to assess for covariate balance before and after matching. Survival curves were compared by Log-rank test and Restricted Mean Survival Time (RMST) at 12 months. AML1718 and AML1310 cohorts differed in terms of age (median: 54 vs 49 years, p=0.003) and risk category (p< .0001) - since the low risk was not represented in AML1718 trial - and female sex (35% vs 48%, p=0.069), though to a lesser extent. Contrariwise, the percentage of transplanted patients was comparable before matching (49% vs 49%, p=0.96). Being more recent, AML1718 median follow-up was shorter than AML1310 (10.5 vs 75.8 months). Full-matching, 1:2 optimal matching and 1:2 genetic matching produced the best balancing. Table 1 shows the results of the analysis for the unmatched and matched data. After balancing, according to all matching methods, the CR rate observed in the AML1718 was significantly higher than AML1310, as well as MRD-negativity rate. Comparing survival outcomes at 12 months, emerged that, upon matching, OS and DFS estimates of the AML1718 were higher than those of AML1310, though a slight statistical significance was reached only with the optimal matching on DFS (p=0.042). This result was confirmed by a statistically significant difference between the two RMST at 12 months (p=0.036). Despite this, a longer AML1718 follow-up is needed to provide a robust comparison between the two protocols. Our propensity-score analysis showed that combining venetoclax with chemotherapy in newly diagnosed AML patients resulted in improved outcomes in terms of CR rate and MRD-negativity: these achievements are crucial to allow transition to allogenic transplantation in first remission. With regards to survival outcomes, a solid conclusion will be drawn when a longer AML1718 follow-up is available. These preliminary results highlight the incremental benefit of venetoclax added to intensive induction chemotherapy and paves the way to novel combination regimens based on venetoclax