Engineering carbon quantum dots for photomediated theranostics

Carbon quantum dots (CQDs) have emerged as potential alternatives to classical metal-based semiconductor quantum dots (QDs) due to the abundance of their precursors, their ease of synthesis, high biocompatibility, low cost, and particularly their strong photoresponsiveness, tunability, and stability. Light is a versatile, tunable stimulus that can provide spatiotemporal control. Its interaction with CQDs elicits interesting responses such as wavelength-dependent optical emissions, charge/electron transfer, and heat generation, processes that are suitable for a range of photomediated bioapplications. The carbogenic core and surface characteristics of CQDs can be tuned through versatile engineering strategies to endow specific optical and physicochemical properties, while conjugation with specific moieties can enable the design of targeted probes. Fundamental approaches to tune the responses of CQDs to photo-interactions and the design of bionanoprobes are presented, which enable biomedical applications involving diagnostics and therapeutics. These strategies represent comprehensive platforms for engineering multifunctional probes for nanomedicine, and the design of QD probes with a range of metal-free and emerging 2D materials.

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Management of stage III breast cancer

The influence of several methodological factors on mean values of sister chromatid exchanges (SCEs) and micronuclei (MN) in peripheral lymphocytes of 1,650 subjects was analyzed. Donors belonged to a general healthy population living in Pisa and in two nearby small cities: Cascina and Navacchio (Ca-Na). Blood samples were collected over a period of 29 months and processed in three different laboratories of the some institute. Slides were analyzed by several scorers. Our data showed that lymphocyte proliferation indexes (PIs) and baseline mean values of SCEs were affected mainly by sampling period. This factor accounted for a percentage ranging from roughly 10% (Pisa) to 20% (Ca-Na) of total SCE variance and from roughly 10% (Pisa) to 13% (Ca-Na) of total PIs variance. A marginal effect was attributable to the different laboratories involved (maximum 3% for SCEs and 7% for PIs). The sampling period variable included many sources of variability such as culture media batches, fetal calf serum, PHA, BrdUrd, and seasonality. MN counts revealed a more marked dependence on processing laboratories. This factor accounted for a percentage of roughly 10% (Pisa and Ca-Na) of total variance, while the sampling period was marginally effective (about 1-4% of total variability). Because laboratories were equipped and supplied with the same materials and consumables and technicians were rotated constantly, the only variable ascertained was represented by the three different models of CO2 incubators used for lymphocyte culturing. When "month" and "incubator" variables were considered jointly, experimental variability accounted for 15-20% of total variance, both for PIs and mean values SCEs and MN. The variability due to slide scoring was reduced by assigning each slide to five different scorers and matching low with high scorers in each group. Present data show that when the study is performed under these controlled conditions, about 20% of total interdonor variability can be explained by experimental or seasonal factors.     

Determination of sulfur dioxide in wine using a quartz crystal microbalance

A new method for the analysis of both total and bound SO(2) in wine is proposed, based on a quartz crystal microbalance (QCM), and it is compared with the widely used Ripper method. The proposed method is faster than the Ripper's, and the instrumentation is either home-made or widely available. When both methods are applied to the same sample, the results obtained using the QCM method are bracketed in an interval less than one-tenth the size of that obtained using the Ripper method. Although the SO(2) concentrations found using the QCM method correlate well with the ones obtained with the Ripper method, the results are systematically higher, which can be explained as due to the absence of interferences known to affect the Ripper method.     

Yearly stepwise increments of the growth hormone dose results in a better growth response after four years in girls with Turner syndrome. Dutch Working Group on Growth Hormone

To optimize the growth promoting effect of growth hormone (GH), 65 previously untreated girls with Turner syndrome (TS), chronological age (CA) 2-11 yr, were randomized into 3 dosage regimen groups: A, B, and C, with a daily recombinant-human GH dose during 4 study years of 4-4-4-4, 4-6-6-6, and 4-6-8-8 IU/m2 b.s. The first GH dosage increase in groups B and C resulted in a significantly higher mean height velocity (HV) compared with constant dose group A. During the third year, when the dose was raised again only in group C, mean HV was significantly higher in groups B and C than in group A, and in group C compared with group B. In year 4 only group C mean HV remained significantly higher than group A. The pattern of change in HSDSCA (Dutch-Swedish-Danish Turner references) was identical; however, in year 4 mean delta HSDSCA in group B also remained significantly higher than group A. After 4 yr GH treatment, the following was determined. 1) The mean delta HSDSCA was significantly higher for groups B and C compared with group A, but not significantly different between groups B and C. 2) Although significantly higher compared with estimated values for untreated Dutch girls with TS, bone maturation of the GH treated girls was not significantly different between groups. 3) It was positively related with the degree of bone age (BA) retardation at start of study and negatively with baseline CA. 4) Both the modified Index of Potential Height (mIPHRUS) and a recently developed Turner-specific final height (FH) prediction method (PTSRUS), based on regression coefficients for H, CA, and bone age, showed significant increases in mean FH prediction, without significant differences between groups. PTSRUS values were markedly higher than the mIPHRUS values. Dose dependency could be shown for the area under the curve (AUC) for GH, but delta HSDSCA was not linearly related with AUC. Baseline GH binding protein (BP) levels were in 84% of the cases within the normal age range; the decrease in mean levels after 6 months GH was not significant. Mean insulin-like growth factor I (IGF-I) and IGFBP-3 plasma levels increased significantly, without significant differences between groups. delta HSDSCA during GH was dependent on IGF-I plasma levels at baseline and during the study period, beta-0.002 and beta-0.0004. Thus, a stepwise GH-dosing approach reduced the "waning" effect of the growth response after 4 yr treatment without undue bone maturation. FH prediction was not significantly different between treatment groups. Irrespective of the GH dose used, initiation of GH treatment at a younger age was beneficial after 4 yr GH when expressed as actual cm gained or as gain in FH prediction, but was not statistically significant when expressed as delta HSDSCA over the study period.     

BOO: Behavior-oriented ontology to describe participant dynamics in collocated design meetings

Facilitating meetings is not an easy task. To assist the facilitator, we have been designing intelligent support systems, which can help contextual sensemaking, decision making and action. However, these systems are constructed based on behavioral models that provide guidelines to understand participant behaviors. This paper presents an ontology to describe participants’ behaviors in collaborative design meetings and rules that correlate them with the group’s acceptance of the final product. This ontology describes the group dynamics at collocated meetings, using verbal and non-verbal cues of attention shifts and attention maintenance as its basic constructs. The objective of creating this ontology was to better understand face-to-face meetings to eventually help meeting facilitators identify issues that may lead to dissatisfaction with the final product through behavioral cues. The ontology was derived through extensive analysis of a series of engineering design session videos. The design group was composed of experts with similar backgrounds, but working in different divisions of the same company. Different points of view were argued and decisions were made at the end of each meeting. After each meeting, participants were asked to asynchronously commit to the decisions made in the group. Our ontology can be used to identify the factors that lead to an undesired outcome, and now serves as a basis for a new project, which uses rules to support design meetings, improve final artifact acceptance and reduce rework. Our conclusions point out correlations between designers’ behaviors and future artifact acceptance and actions that interrupt or bring back group attention. The ontology was validated through application to other meeting situations. These findings may guide software developers in the creation of tools to support group design, and may be applied by an intelligent system.     

Applying machine learning techniques for scaling out data quality algorithms in cloud computing environments

Deduplication is the task of identifying the entities in a data set which refer to the same real world object. Over the last decades, this problem has been largely investigated and many techniques have been proposed to improve the efficiency and effectiveness of the deduplication algorithms. As data sets become larger, such algorithms may generate critical bottlenecks regarding memory usage and execution time. In this context, cloud computing environments have been used for scaling out data quality algorithms. In this paper, we investigate the efficacy of different machine learning techniques for scaling out virtual clusters for the execution of deduplication algorithms under predefined time restrictions. We also propose specific heuristics (Best Performing Allocation, Probabilistic Best Performing Allocation, Tunable Allocation, Adaptive Allocation and Sliced Training Data) which, together with the machine learning techniques, are able to tune the virtual cluster estimations as demands fluctuate over time. The experiments we have carried out using multiple scale data sets have provided many insights regarding the adequacy of the considered machine learning algorithms and proposed heuristics for tackling cloud computing provisioning.     

Supporting Unified Distributed Management and Autonomic Decisions: Design,Implementation and Deployment

Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of management information; (B) Autonomic Decision System (ADS) that performs distributed decisions in the network with incomplete information. We deploy the UDNM framework in a testbed which involves two cities (\(\approx\)250 km between), different standards (IEEE 802.3, IEEE 802.11 and IEEE 802.16e) and network technologies, such as, wired virtual grid, wireless ad-hoc gateways, ad-hoc mobile access devices. The UDNM framework integrates management functionalities into the managed devices, proving to be a lightweight and easy-respond framework. The performance analysis shows that the UDNM framework is feasible to unify devices management functionalities and to take accurate decisions on top of a real network.     

Robotic control in knee joint replacement surgery

A brief history of robotic systems in knee arthroplasty is provided. The place of autonomous robots is then discussed and compared to more recent 'hands-on' robotic systems that can be more cost effective. The case is made for robotic systems to have a clear justification, with improved benefits compared to those from cheaper navigation systems. A number of more recent, smaller, robot systems for knee arthroplasty are also described. A specific example is given of an active constraint medical robot, the ACROBOT system, used in a prospective randomized controlled trial of unicondylar robotic knee arthroplasty in which the robot was compared to conventional surgery. The results of the trial are presented together with a discussion of the need for measures of accuracy to be introduced so that the efficacy of the robotic surgery can be immediately identified, rather than have to wait for a number of years before long-term clinical improvements can be demonstrated.     

Analysis of factors contributing to success in a program of micromanipulation-assisted fertilization

OBJECTIVE: To determine factors important to clinical success in micromanipulation-assisted in vitro fertilization (IVF). DESIGN: Procedures invoked in two separate series of micromanipulation-assisted IVF cycles, one unsuccessful (series I) and the other successful (series II), were compared in an effort to identify changes that led to clinical success. SETTING: University-based IVF clinic. PATIENTS: In both IVF series involving micromanipulation, patients consisted of infertile couples who fit any of five categories of male-factor related infertility. The female patients underwent controlled hyperstimulation for oocyte retrieval and the oocytes were inseminated normally or were subjected either to partial zona dissection or subzonal sperm insertion to assist fertilization. Results in all groups were compared between the two patient series. RESULTS: In the successful series II, a noticeable improvement in fertilization rate and embryo quality was observed compared with series I. A significant increase in the percentage of patients reaching embryo transfer, the pregnancy rate per transfer, and the pregnancy rate per retrieval were noted in series II; a 25% ongoing pregnancy rate per retrieval was observed overall in this successful group, with "ongoing" defined as manifestation of at least a fetal sac on ultrasound with no detectable problems. Patients with a mixed transfer of embryos derived from manipulated and normally inseminated oocytes had a 75% rate of pregnancy per transfer in series II. Differences between the two series could not be attributed to patient selection or biases in selection of oocytes relegated to micromanipulation. However, oocyte handling, micromanipulation, and culture protocols differed significantly between the two series in that temperature and pH of oocytes was better controlled, and micromanipulation time was minimized in series II. CONCLUSION: Success in micromanipulation depends on maintenance of the oocyte in a stable and supportive environment throughout the micromanipulation procedure. It is also important to minimize trauma to the eggs by performing micromanipulation rapidly and with minimal distortion of the egg. Patients with a poor fertilization rate in standard IVF may experience a substantial increase in the likelihood of pregnancy when micromanipulation-assisted fertilization is performed on some eggs.