Lymphatic drainage mapping with indirect lymphography for canine mammary tumors

Mammary gland tumors are the most common canine neoplasms. They account for 25– 50% of all tumors diagnosed in bitches. Metastases and recurrences develop in about 35–70% of bitches following excision. The presence of regional lymph node metastases is a relevant factor affecting prognosis and treatment in cases of mammary gland tumors. The sentinel lymph node (SLN) is the first lymph node (or nodes) in the regional lymphatic basin that receives lymphatic flow from the primary neoplasm. The aim of this study is to investigate the SLN with indirect lymphography for a mammary tumor in dogs. The knowledge of the precise drainage pattern and SLN of the neoplastic mammary glands would provide clinically relevant information to the surgeon and to the oncologist, and it would be of high importance for the surgeon not only for performing the most adequate surgical excision but also for determining an accurate post-surgical prognosis

Autonomic nervous system responses to strength training in top-level weight lifters

In athletes, spectral analysis of HR variability (HRV) has been shown capable to detect the adaptational changes in sympatho-vagal control attending physical training. So far, studies investigated autonomic nervous system (ANS) changes occurring with endurance training, whereas adaptations to markedly different exercise modes, for example, strength training, have never been investigated. We assessed the changes in cardiac ANS parameters during long-term training in weight lifters of the Italian team preparing for the European Championship, where athletes competed for obtaining the pass for Olympic Games. We investigated nine athletes. Subject trained 3 sessions/day, 6 days a week. The intensity of strength exercises varied from 70% to 95% 1 RM. Training load (TL) was calculated as: volume (min)  7 intensity (%1RM).All ANS parameters were significantly and highly correlated on an individual basis to the dose of exercise with a second-order regression model (r2 ranged from 0.96 to 0.99; P < 0.001). The low-frequency (LF) component of HRV and LF/HF ratio showed an initial increase with the progression of TL and then a decrease, resembling a bell-shaped curve with a minimum at the highest TL. The high-frequency (HF) component of HRV and R-R interval showed a reciprocal pattern, with an initial decrease with progression of TL followed by an increase, resembling an U-shaped curve with a maximum at the highest TL. These adaptations were at the opposite to those previously reported in endurance athletes. These results suggest that in Olympic weight lifters, ANS adaptations to training are dose-related on individual basis and that ANS adaptations are mainly sport-specific

Rapid identification of BCR/ABL1-like acute lymphoblastic leukaemia patients using a predictive statistical model based on quantitative real time-polymerase chain reaction: clinical, prognostic and therapeutic implications.

BCR/ABL1-like acute lymphoblastic leukaemia (ALL) is a subgroup of B-lineage acute lymphoblastic leukaemia that occurs within cases without recurrent molecular rearrangements. Gene expression profiling (GEP) can identify these cases but it is expensive and not widely available. Using GEP, we identified 10 genes specifically overexpressed by BCR/ABL1-like ALL cases and used their expression values - assessed by quantitative real time-polymerase chain reaction (Q-RT-PCR) in 26 BCR/ABL1-like and 26 non-BCR/ABL1-like cases to build a statistical "BCR/ABL1-like predictor", for the identification of BCR/ABL1-like cases. By screening 142 B-lineage ALL patients with the "BCR/ABL1-like predictor", we identified 28/142 BCR/ABL1-like patients (19·7%). Overall, BCR/ABL1-like cases were enriched in JAK/STAT mutations (P < 0·001), IKZF1 deletions (P < 0·001) and rearrangements involving cytokine receptors and tyrosine kinases (P = 0·001), thus corroborating the validity of the prediction. Clinically, the BCR/ABL1-like cases identified by the BCR/ABL1-like predictor achieved a lower rate of complete remission (P = 0·014) and a worse event-free survival (P = 0·0009) compared to non-BCR/ABL1-like ALL. Consistently, primary cells from BCR/ABL1-like cases responded in vitro to ponatinib. We propose a simple tool based on Q-RT-PCR and a statistical model that is capable of easily, quickly and reliably identifying BCR/ABL1-like ALL cases at diagnosis

A soft wearable robot for tremor assessment and suppression

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High Accuracy Mutation Detection in Leukemia on a Selected Panel of Cancer Genes

<div><p>With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using Nimblegen sequence capture coupled with Roche/454 technology. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely <em>BWA-SW</em> and <em>SSAHA2</em>, coupled with the variant calling algorithm <em>Atlas-SNP2</em> yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.</p> </div

The Relevance of Nationality and Industry for Stakeholder Salience: An Investigation Through Integrated Reports

The aim of this research is to investigate the web of business-stakeholder relationships emerging from first integrated reports. Drawn from the stakeholder salience theory, the analysis focuses on some factors that may cause specific stakeholders to be crucial for some organizations and their ability to create value over time. More precisely, findings highlight the importance of industry membership, while entities’ nationality seems not to be a differentiating element. This study contributes to the corporate disclosure literature by analyzing an emerging reporting tool, the integrated report, and demonstrating that the link between some business characteristics and stakeholder salience seems fundamental for the representation of the impact of corporate social and environmental responsibilities on the economic performance. From a practical point of view, the impact of industry membership on corporate disclosures encourages the drafting of differentiated reporting standards across sectors, in order to improve comparability, materiality, and reliability of information

Cardiac Autonomic Modulation In Olympic Triathletes Before Olympic Pre-Event

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Design of a TLM NAND flash controller model for audio real-time applications

The widespread use of electronic equipments such as smartphones, tablets and digital cameras is largely contributing to the relentless progress of memories devices based on nonvolatile flash technology. Volatile memories, typically based on DRAM technology, are characterized by higher cost and performance when compared to non-volatile memories; in the design of an electronic device it is important to balance the utilization of these two storage solutions to meet different needs in terms of processing speed and long-term data retention. This paper reports the development of the system-level model of a controller capable of optimizing the use of NAND type flash memories, for the storage and the playback of audio samples in real-time music applications; the aim is the reduction of the quantity of system SDRAM memory thus lowering the cost of the final product, while still providing the user with the most highfidelity sound experience

Asymptotically exact AM-FM decomposition based on iterated Hilbert transform

This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented

Computational intelligence for the collaborative identification of distributed systems

In this chapter, on the basis of a rigorous mathematical formulation, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm extends a KLT-based identification approach to a decentralized setting, using the distributed Karhunen-Loéve transform (DKLT) recently proposed by Gastpar et al.. The proposed approach permits an arbitrarily accurate identification since it exploits both the asymptotic properties of convergence of DKLT and the universal approximation capabilities of radial basis functions neural networks. The effectiveness of the proposed approach is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy, as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. Some identification experiments, that have been carried out on systems whose behavior is described by partial differential equations in 2-D domains with random excitations, confirm the validity of this approach. It is worth noting the generality of the algorithm that can be applied in a wide range of applications without limitations on the type of physical phenomena, boundary conditions, sensor network used, and number of its nodes