A Rose of Hope

Superhydrophobic aluminum created by coating floral nanostructured 7075 (Colored SEM Image) aluminum with silanes can be used to increase the efficiency of HVAC systems. Creating such efficient technologies has the potential to ultimately decrease fuel consumption hence reducing carbon emission. Climate change being a great threat to humanity even such small steps may raise our hope for a beautiful future just like the first rose in a garden.Ope

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

How are compassion fatigue, burnout, and compassion satisfaction affected by quality of working life? Findings from a survey of mental health staff in Italy

BACKGROUND: Quality of working life includes elements such as autonomy, trust, ergonomics, participation, job complexity, and work-life balance. The overarching aim of this study was to investigate if and how quality of working life affects Compassion Fatigue, Burnout, and Compassion Satisfaction among mental health practitioners. METHODS: Staff working in three Italian Mental Health Departments completed the Professional Quality of Life Scale, measuring Compassion Fatigue, Burnout, and Compassion Satisfaction, and the Quality of Working Life Questionnaire. The latter was used to collect socio-demographics, occupational characteristics and 13 indicators of quality of working life. Multiple regressions controlling for other variables were undertaken to predict Compassion Fatigue, Burnout, and Compassion Satisfaction. RESULTS: Four hundred questionnaires were completed. In bivariate analyses, experiencing more ergonomic problems, perceiving risks for the future, a higher impact of work on life, and lower levels of trust and of perceived quality of meetings were associated with poorer outcomes. Multivariate analysis showed that (a) ergonomic problems and impact of work on life predicted higher levels of both Compassion Fatigue and Burnout; (b) impact of life on work was associated with Compassion Fatigue and lower levels of trust and perceiving more risks for the future with Burnout only; (c) perceived quality of meetings, need of training, and perceiving no risks for the future predicted higher levels of Compassion Satisfaction. CONCLUSIONS: In order to provide adequate mental health services, service providers need to give their employees adequate ergonomic conditions, giving special attention to time pressures. Building trustful relationships with management and within the teams is also crucial. Training and meetings are other important targets for potential improvement. Additionally, insecurity about the future should be addressed as it can affect both Burnout and Compassion Satisfaction. Finally, strategies to reduce possible work-life conflicts need to be considered

Fuzzy Logic Controller Design for Intelligent Air-Conditioning System

Inefficient air cooling systems may cause of wasting energy in a great amount specially in the urban area. Being the most popular cooling system, air-conditioners have been used in domestic usage as well as in industrial applications. However, the unintelligent nature of such cooling system gives rise to excess energy consumption which causes a huge problem in the current energy hungry world. In this paper, we present design of a fuzzy logic controller for the intelligent air-conditioning system. The performance of the controller is also simulated. The proposed controller has the adaptive nature to control fan and compressor speed which leads to reducing power consumption. Also, the system controls the operation mode to retain the healthy oxygen level and humid condition of the indoor environment

Effect of COVID-19 pandemic on utilisation of community-based mental health care in North-East of Italy: A psychiatric case register study

Aims: WHO declared that mental health care should be considered one essential health service to be maintained during the coronavirus disease 2019 (COVID-19) pandemic. This study aims to describe the effect of lockdown and restrictions due to the COVID-19 pandemic in Italy on mental health services' utilisation, by considering psychiatric diagnoses and type of mental health contacts. Methods: The study was conducted in the Verona catchment area, located in the Veneto region (northeastern Italy). For each patient, mental health contacts were grouped into: (1) outpatient care, (2) social and supportive interventions, (3) rehabilitation interventions, (4) multi-professional assessments, (5) day care. A 'difference in differences' approach was used: difference in the number of contacts between 2019 and 2020 on the weeks of lockdown and intermediate restrictions was compared with the same difference in weeks of no or reduced restrictions, and such difference was interpreted as the effect of restrictions. Both a global regression on all contacts and separate regressions for each type of service were performed and Incidence Rate Ratios (IRRs) were calculated. Results: In 2020, a significant reduction in the number of patients who had mental health contacts was found, both overall and for most of the patients' characteristics considered (except for people aged 18-24 years for foreign-born population and for those with a diagnosis of schizophrenia. Moreover, in 2020 mental health contacts had a reduction of 57 096 (-33.9%) with respect to 2019; such difference remained significant across the various type of contacts considered, with rehabilitation interventions and day care showing the greatest reduction. Negative Binomial regressions displayed a statistically significant effect of lockdown, but not of intermediate restrictions, in terms of reduction in the number of contacts. The lockdown period was responsible of a 32.7% reduction (IRR 0.673; p-value <0.001) in the overall number of contacts. All type of mental health contacts showed a reduction ascribable to the lockdown, except social and supportive interventions. Conclusions: Despite the access to community mental health care during the pandemic was overall reduced, the mental health system in the Verona catchment area was able to maintain support for more vulnerable and severely ill patients, by providing continuity of care and day-by-day support through social and supportive interventions

Machine learning-enabled multiplexed microfluidic sensors

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed

Cassava haplotype map highlights fixation of deleterious mutations during clonal propagation

Article purchased; Published online: 17 April 2017Cassava (Manihot esculenta Crantz) is an important staple food crop in Africa and South America; however, ubiquitous deleterious mutations may severely decrease its fitness. To evaluate these deleterious mutations, we constructed a cassava haplotype map through deep sequencing 241 diverse accessions and identified >28 million segregating variants. We found that (i) although domestication has modified starch and ketone metabolism pathways to allow for human consumption, the concomitant bottleneck and clonal propagation have resulted in a large proportion of fixed deleterious amino acid changes, increased the number of deleterious alleles by 26%, and shifted the mutational burden toward common variants; (ii) deleterious mutations have been ineffectively purged, owing to limited recombination in the cassava genome; (iii) recent breeding efforts have maintained yield by masking the most damaging recessive mutations in the heterozygous state but have been unable to purge the mutation burden; such purging should be a key target in future cassava breeding

Fuzzy Logic Controller Design for Intelligent Drilling System

An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlikethe existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation

Nanomechanics of Streptavidin Hubs for Molecular Materials

A new strategy is reported for creating protein-based nanomaterials by genetically fusing large polypeptides to monomeric streptavidin and exploiting the propensity of streptavidin monomers(SM) to self-assemble into stable tetramers. We have characterized the mechanical properties of streptavidin-linked structures and measured, for the first time, the mechanical strength of streptavidin tetramers themselves. Using streptavidin tetramers as molecular hubs offers a unique opportunity to create a variety of well-defined, self-assembled protein-based (nano) materials with unusual mechanical properties