Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Utility of Survivin (BIRC5), Novel Cardiac Biomarker, as a Prognostic Tool Compared to High-sensitivity C-reactive Protein, Heart-type Fatty Acid Binding Protein and Revised Lee Score in Elderly Patients Scheduled for Major Non-cardiac Surgery: A Prospective Pilot Study

Summary Background: Recent studies indicate that survivin (BIRC5) is sensitive to the existence of previous ischemic heart disease, since it is activated in the process of tissue repair and angiogenesis. The aim of this study was to determine the potential of survivin (BIRC5) as a new cardiac biomarker in the preoperative assessment of cardiovascular risk in comparison with clinically accepted cardiac biomarkers and one of the relevant clinical risk scores. Methods: We included 79 patients, female (41) and male (38), with the mean age of 71.35±6.89. Inclusion criteria: extensive non-cardiac surgery, general anesthesia, age >55 and at least one of the selected cardiovascular risk factors (hypertension, diabetes mellitus, hyperlipidemia, smoking and positive family history). Exclusion criteria: emergency surgical procedures and inability to understand and sign an informed consent. Blood sampling was performed 7 days prior surgery and levels of survivin (BIRC5), hsCRP and H-FABP were measured. Results: Revised Lee score was assessed based on data found in patients’ history. Levels of survivin (BIRC5) were higher in deceased patients (P<0.05). It showed AUC=0.807 (95% CI, P<0.0005, 0.698–0.917), greater than both H-FABP and revised Lee index, and it increases the mortality prediction when used together with both biomarkers and revised Lee score. The determined cut-off value was 4 pg/mL and 92.86% of deceased patients had an increased level of survivin (BIRC5), (P=0.005). Conclusions: Survivin (BIRC5) is a potential cardiac biomarker even in elderly patients without tumor, but it cannot be used independently. Further studies with a greater number of patients are needed.
High-resolution anorectal manometry in children with functional constipation: a single-centre experience before and after treatment

Introduction Constipation is a common disorder among children, and most of the cases are functional in aetiology. Few studies have reported the manometric data of normal and constipated children. Aim To evaluate the manometric parameters in children with functional constipation and to assess any possible changes in these parameters after treatment. Material and methods A prospective descriptive study was conducted at a single centre, enrolling 50 children diagnosed with functional constipation based on Rome IV criteria. Their age ranged from 6 to 14 years with a mean of 7.31 ±1.72 years. High-resolution manometry was performed on all children at the initial presentation and after six months of treatment. Results The studied children showed markedly abnormal rectal sensation parameters (increased first sensation, first urge, intense urge, and maximum tolerable volume) during rectal balloon distension. These parameters were even higher in children with stool incontinence (p = 0.005). Manometric data after 6 months of treatment showed that the resting and squeeze pressures were increased when compared to pre-treatment recordings; however, both were statistically insignificant (p = 0.474 and p = 0.155, respectively). Abnormalities in rectal sensations and the manometric parameters reached near normal values following treatment. Conclusions Anorectal manometry is sensitive in predicting improvement in patient condition even before complete clinical cure, and it has a prognostic role in the management of childhood constipation. More research is still needed before recommending anorectal manometry as a routine diagnostic or prognostic tool in paediatric constipation management.
Development of a scale for identifying autism spectrum disorder during early childhood

Context: Assessment forms an important part of the early intervention in autism spectrum disorders (ASD). However, there is not much choice with regard to tools and methods for early identification and assessment of ASD. Aims: The aim is to develop a scale for identifying ASD during infancy and early childhood by mapping empirically supported skill behaviors and excess behaviors. Settings and Design: The study was conducted in community-based organisations and the local communities. Moreover, it employed a post facto research design, with survey method. Materials and Methods: Following the standard procedures to pool items and standardization, a scale was developed and administered to three groups of children (N = 190) including children with autism (n = 100), global developmental delay suggestive of mental retardation (n = 40) and neurotypicals (n = 50) in the age range of 1.5–6 years. The ICD-10 Classification of Mental and Behaviooural Disorders and Baby and Infant Screen for Children with aUtIsm Traits (BISCUIT) were the gold standards to diagnose ASD. Statistical Analysis Used: Descriptive statistics (frequency, percentages, median, and quartile deviation), Pearson's correlation, Cronbach's α, factor analysis, and binary logistic regression analysis with receiver operating characteristic curve were performed. Results: The new scale also demonstrated a high diagnostic efficiency by yielding a hit rate of 0.89, specificity of 0.90, and sensitivity of 0.88. Conclusions: The new scale can be used for early identification of ASD in the Indian population, though further validation with large population is required.
Neuropeptide and Small Transmitter Coexistence: Fundamental Studies and Relevance to Mental Illness

Neuropeptides are auxiliary messenger molecules that always co-exist in nerve cells with one or more small molecule (classic) neurotransmitters. Neuropeptides act both as transmitters and trophic factors, and play a role particularly when the nervous system is challenged, as by injury, pain or stress. Here neuropeptides and coexistence in mammals are reviewed, but with special focus on the 29/30 amino acid galanin and its three receptors GalR1, -R2 and -R3. In particular, galanin’s role as a co-transmitter in both rodent and human noradrenergic locus coeruleus (LC) neurons is addressed. Extensive experimental animal data strongly suggest a role for the galanin system in depression–like behavior. The translational potential of these results was tested by studying the galanin system in postmortem human brains, first in normal brains, and then in a comparison of five regions of brains obtained from depressed people who committed suicide, and from matched controls. The distribution of galanin and the four galanin system transcripts in the normal human brain was determined, and selective and parallel changes in levels of transcripts and DNA methylation for galanin and its three receptors were assessed in depressed patients who committed suicide: upregulation of transcripts, e.g., for galanin and GalR3 in LC, paralleled by a decrease in DNA methylation, suggesting involvement of epigenetic mechanisms. It is hypothesized that, when exposed to severe stress, the noradrenergic LC neurons fire in bursts and release galanin from their soma/dendrites. Galanin then acts on somato-dendritic, inhibitory galanin autoreceptors, opening potassium channels and inhibiting firing. The purpose of these autoreceptors is to act as a ‘brake’ to prevent overexcitation, a brake that is also part of resilience to stress that protects against depression. Depression then arises when the inhibition is too strong and long lasting – a maladaption, allostatic load, leading to depletion of NA levels in the forebrain. It is suggested that disinhibition by a galanin antagonist may have antidepressant activity by restoring forebrain NA levels. A role of galanin in depression is also supported by a recent candidate gene study, showing that variants in genes for galanin and its three receptors confer increased risk of depression and anxiety in people who experienced childhood adversity or recent negative life events. In summary, galanin, a neuropeptide coexisting in LC neurons, may participate in the mechanism underlying resilience against a serious and common disorder, MDD. Existing and further results may lead to an increased understanding of how this illness develops, which in turn could provide a basis for its treatment.
The Role of Co-stimulatory/Co-inhibitory Signals in Graft-vs.-Host Disease

Allogeneic hematopoietic cell transplantation (allo-HCT) is an effective immunotherapeutic approach for various hematologic and immunologic ailments. Despite the beneficial impact of allo-HCT, its adverse effects cause severe health concerns. After transplantation, recognition of host cells as foreign entities by donor T cells induces graft-vs.-host disease (GVHD). Activation, proliferation and trafficking of donor T cells to target organs and tissues are critical steps in the pathogenesis of GVHD. T cell activation is a synergistic process of T cell receptor (TCR) recognition of major histocompatibility complex (MHC)-anchored antigen and co-stimulatory/co-inhibitory signaling in the presence of cytokines. Most of the currently used therapeutic regimens for GVHD are based on inhibiting the allogeneic T cell response or T-cell depletion (TCD). However, the immunosuppressive drugs and TCD hamper the therapeutic potential of allo-HCT, resulting in attenuated graft-vs.-leukemia (GVL) effect as well as increased vulnerability to infection. In view of the drawback of overbroad immunosuppression, co-stimulatory, and co-inhibitory molecules are plausible targets for selective modulation of T cell activation and function that can improve the effectiveness of allo-HCT. Therefore, this review collates existing knowledge of T cell co-stimulation and co-inhibition with current research that may have the potential to provide novel approaches to cure GVHD without sacrificing the beneficial effects of allo-HCT.
Machine Learning for Automatic Resource Management in the Datacenter and the Cloud

Author(s): Yadwadkar, Neeraja Jayant | Advisor(s): Katz, Randy H. ; Gonzalez, Joseph E. | Abstract: Traditional resource management techniques that rely on simple heuristics often fail to achieve predictable performance in contemporary complex systems that span physical servers, virtual servers, private and/or public clouds. My research aims to bring the benefits of data-driven models to resource management of such complex systems. In my dissertation, I argue that the advancements in machine learning can be leveraged to manage and optimize today's systems by deriving actionable insights from the performance and utilization data these systems generate. To realize this vision of model-based resource management, we need to deal with the key challenges data-driven models raise: uncertainty in predictions, cost of training, generalizability from benchmark datasets to real-world systems datasets, and interpretability of the models.In this dissertation, to demonstrate how to handle these challenges, we chose two main problem domains: (I) Scheduling in parallel data intensive computational frameworks for improved tail latencies, and (II) Performance-aware resource allocation in the public cloud environments for meeting user-specified performance and cost goals.We begin by presenting Wrangler, a system that predicts when stragglers (slow-running tasks) are going to occur based on cluster resource utilization counters and makes scheduling decisions to avoid such situations. Wrangler introduces a notion of a confidence measure with these predictions to overcome modeling uncertainty. We then describe our Multi-Task Learning formulations that share information between the various models, allowing us to significantly reduce the cost of training. To capture the challenges of resource allocation in the public cloud environments, we present key observations from our empirical analysis based on performance profiles of workloads executing across different public cloud environments. Finally, we describe PARIS, a Performance-Aware Resource Inference System, that we built to enable cloud users to select the best VM (virtual machine) for their applications in the public cloud environments so as to satisfy any performance and cost constraints.
LISA '11: Theme - "DevOps: New Challenges, Proven Values"

SYSADMIN The theme for LISA '11 is " DevOps: New Challenges, Proven Values. " DevOps is " an umbrella concept that refers to anything that improves the interaction between development and operations " [1]. While usually associated with Web operations , the tools and techniques are now being mainstreamed into the enterprise. There is an important shift happening in system administration, and we felt it was important to acknowledge this change by making it this year's theme. What is this change? In the status quo, sysadmins obtain software from vendors and struggle with operational issues with varying levels of support. Scaling, disaster recovery, operational efficiency is left as an exercise for the user. Recently there has been a trend towards self-sourcing. A company providing a Web-based service develops it in-house and the operations work is done by in-house system administrators. Development and Operations work together, with shared responsibility for the success of the whole. This last part bears repeating: shared responsibility. Let's call this new way " DevOps. " While we're at it, let's acknowledge that it isn't new. However, it is becoming more frequent and is the dominant paradigm for the high-growth segments of our industry. As a result, new cultural " best practices " are becoming apparent. While in-house software development is nothing new, the Web-centric world creates opportunities that hadn't existed before. Likewise, you can manage a new-style, Web-based service with the paradigm of the past, but you would miss out on opportunities that were unavailable before. For example, the old way often involves packaged, shrink-wrapped software. A new release comes along each year. The effort to ship a new release is huge. You have a printer that makes boxes, a factory that produces media, an assembly line that puts them all together and ships them. Every new release involves a new manual, a new box, and inventory strategies to deal with the old version sitting in the warehouse. All these pieces come together once a year. To make it all happen we use software development methodologies with names like " Waterfall, " " Spiral, " and " Release Trains. " If there are millions of users, there are millions of deployments. In the Web world, shipping a new release has much lower overhead. There is no physical package .
A Type-I Sub-Sampling PLL With a  $100\times100\,\,\mu\text{m}^{2}$  Footprint and −255-dB FOM

A dual-loop <italic>LC</italic>-voltage-controlled oscillator (VCO)-based frequency synthesizer, composed of an all-digital frequency-locked loop (ADFLL) and a voltage-mode, type-I, sub-sampling phase-locked loop (SS-PLL), is presented. A compact SS phase detector is described which also acts as a loop filter (LF). Fabricated in a 65-nm CMOS process, the synthesizer occupies a small footprint of <inline-formula> <tex-math notation="LaTeX">$100 \times 100\,\,\mu \text{m}^{2}$ </tex-math></inline-formula>, thanks to its compact LF and full integration underneath the VCO inductor. The synthesizer achieves the sub-200 fs of rms integrated jitter across its tuning range of 4.6–5.6 GHz while consuming no more than 1.1 mW of power. A peak figure-of-merit (FOM) of −255 dB at 5 GHz and an FOM of −254 dB across the tuning range are achieved. A reference spur of −64.1 dBc is measured with the PLL operating at 5 GHz.
Online Parameters Estimation and Autotuning of a Discrete-Time Model Predictive Speed Controller for Induction Motor Drives

This paper proposes a new method that can online and automatically estimate and fine-tune the parameters of a discrete-time model predictive controller for providing high-performance speed control in an induction motor (IM) drive. The suggested control system combines the model reference adaptive method with the fuzzy-logic technique, and its operation can be initiated without requiring any human intervention or the knowledge of the motor drive parameters. Therefore, no extra work by the user is needed to adjust the controller parameters according to the operating conditions, and also high performance is attained because any variations of the system model can be considered through a fine-tuning procedure. The proposed autoadaptive discrete-time model predictive control (ADMPC) system is based on the optimization of an objective function that considers the reference and the real speed as well as the acceleration of the IM drive by using the state-space model. The implementation of the proposed ADMPC scheme is easy, since no additional hardware is required, but only the replacement of the firmware of the IM drive. Selective simulation and experimental results are presented to validate the effectiveness of the proposed ADMPC system and demonstrate the high performance of the motor drive.
Transverse Modes in I.H.P. SAW Resonator and Their Suppression Method

We discuss transverse modes in a resonator on a multilayer substrate with a thin piezoelectric plate. A generation mechanism of the transverse mode response in I.H. P. SAW (Incredible High-Performance SAW)resonator with a LiTa03 plate is analyzed by using FEM simulations, and it is shown that a slowness curve of the I.H. P. SAW structure is changed significantly compared with a standard 42° Y-X LiTa03 substrate. A new tilted resonator structure is proposed to suppress the transverse mode spurious. It is found that the tilted resonator with a small tilted angle can suppress the spurious response successfully with maintaining a high-Q factor.
Modelling of Channels for Intra-Corporal Ultrasound Communication

Ultrasound waves have been shown to have advantages over radio frequency waves for intra-corporal communication. However, the development of communication protocols is hampered by the lack of models for the communication channels. Therefore, a propagation model was introduced including effects of attenuation, diffraction, and multipath. By assuming rectangular enclosure media with point sources and receivers implanted, critical metrics were extracted from the simulated channel impulse response, and the results were verified by corresponding experimental measurements with good accuracy. This work laid foundations of a computer simulation model to predict channel behaviour for the purpose of intra-corporal ultrasonic communications.
Feasibility of Genetic Algorithms in 2D Ultrasound Array Optimization

The optimization of 2D ultrasound arrays can be efficiently realized through the use of simulated annealing (SA) as reported in many recent works. The main limitation of SA resides in the amount of resources required and the optimization time. In this work, the application of the genetic algorithms (GA) in 2D arrays optimization is presented to evaluate the potential of these algorithms compared to the simulated annealing in terms of processing time and output beam profiles. GA have been used in the optimization of multivariable cost functions in many applications as power systems analysis, wireless sensor arrays in telecommunication and they provided satisfactory results in a relative short time. The 2D ultrasound arrays optimization can also be formulated in a similar way as the problems occurring in these domains, making genetic algorithms a potential candidate to solve them. Although GA are reported to not be as efficient as SA in the optimization of medium and large 2D arrays, the aim of this paper is to evaluate their real ability when compared to the SA. Preliminary results showed a fast convergence time of genetic algorithm for comparable acoustical properties for the optimized arrays compared to SA.
Text Line Extraction Based on Distance Map Features and Dynamic Programming

Text Line Segmentation is a basic document layout task that consists in detecting and extracting the text lines present in a document page image. Although considered a basic task, generally, it is a necessary step for Handwritten Text Recognition (HTR) higher level tasks. Most state of the art automatic text recognition, text-to-line image alignment and key word spotting systems require it due to their need for isolated text line images as input. Traditionally most Text Line Segmentation approaches cover both detection and extraction sub steps. However, the community has recently shifted its focus to tackle independently the baseline detection in document images. This shift generates the need for extraction methods that use these detected baselines as input. In this paper, a binarization free dynamic programming approach that generates an equidistant text line extraction polygon is presented. The approach performs this calculation, based on the information provided by priorly detected text baselines and automatically generated foreground pixels distance maps. We evaluate our approach both in a synthetic competition corpus and in a challenging real handwritten text recognition task corpus. We evaluate it not only at the graphical error level but also the impact it produces on an HTR task trained with the line images it yields. We compare our solution with other solutions ranging from the actual human reviewed ground-truth polygons to simpler automatic generated rectangle areas.
Sample-Data-Based Stabilization of a Class of Switched Nonlinear Systems with Unknown Output Function

In this paper, the problem of sampled-data output feedback control is discussed for a class of switched nonlinear systems with imprecise output information. First, with the help of a compensator, an output feedback controller in the form of continuous-time is designed on the basis of backstepping design method and the domination approach. Then, by discretizing the continuous-time one, we obtain the sampled-data output feedback controller by skillfully choosing design parameters and a maximum sampling period, which ensures the global exponential stability for the closed-loop system. Finally, a numerical example is presented to illustrate the validity of the result.
Secure Session Key Management Scheme for Meter-Reading System Based on LoRa Technology

Long range (LoRa) technology is a wireless communication technology, which may provide low-power, low-rate, and LoRa communication. It may widely be used into many applications, such as smart metering and factory monitoring. However, some features of LoRa technology also bring new security weaknesses. Therefore, we propose an improved session key management to secure the meter-reading system based on LoRa technology. In our proposed scheme, we add a trusted key distribution server for the related devices to generate their session keys and manage all the keys, where the symmetric session keys are used to protect the communication data between the servers and the terminal devices. In addition, in order to maintain the long-term security of the proposed meter reading system, our proposed lightweight key management scheme can update the session keys automatically and remotely. Finally, we make analysis and experiments to evaluate our proposed key management scheme for the meter-reading system, the related results show our proposed scheme is secure and effective.