nils-herrmann/clean_scirepeval huggingface dataset:
Robust whole-brain segmentation: Application to traumatic brain injury

We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.
Cloud Services vs. On-Premise Solutions Cost Comparison Calculator

There are different methods that can be used by product managers in order to set the subscription price of cloud services. No matter the methodology used, the price of a cloud service has to be able to stand against the total-costof-ownership of the cloud service on-premise equivalent. Calculating the cost difference between a cloud service and its on-premise equivalent can be a valuable tool for both customers and product managers. This paper will try to develop a Cloud vs. On-Premise Cost Comparison Calculator.
Nusinersen versus Sham Control in Later-Onset Spinal Muscular Atrophy.

Nusinersen is an antisense oligonucleotide drug that modulates pre-messenger RNA splicing of the survival motor neuron 2 ( SMN2) gene. It has been developed for the treatment of spinal muscular atrophy (SMA).



We conducted a multicenter, double-blind, sham-controlled, phase 3 trial of nusinersen in 126 children with SMA who had symptom onset after 6 months of age. The children were randomly assigned, in a 2:1 ratio, to undergo intrathecal administration of nusinersen at a dose of 12 mg (nusinersen group) or a sham procedure (control group) on days 1, 29, 85, and 274. The primary end point was the least-squares mean change from baseline in the Hammersmith Functional Motor Scale-Expanded (HFMSE) score at 15 months of treatment; HFMSE scores range from 0 to 66, with higher scores indicating better motor function. Secondary end points included the percentage of children with a clinically meaningful increase from baseline in the HFMSE score (≥3 points), an outcome that indicates improvement in at least two motor skills.



In the prespecified interim analysis, there was a least-squares mean increase from baseline to month 15 in the HFMSE score in the nusinersen group (by 4.0 points) and a least-squares mean decrease in the control group (by -1.9 points), with a significant between-group difference favoring nusinersen (least-squares mean difference in change, 5.9 points; 95% confidence interval, 3.7 to 8.1; P<0.001). This result prompted early termination of the trial. Results of the final analysis were consistent with results of the interim analysis. In the final analysis, 57% of the children in the nusinersen group as compared with 26% in the control group had an increase from baseline to month 15 in the HFMSE score of at least 3 points (P<0.001), and the overall incidence of adverse events was similar in the nusinersen group and the control group (93% and 100%, respectively).



Among children with later-onset SMA, those who received nusinersen had significant and clinically meaningful improvement in motor function as compared with those in the control group. (Funded by Biogen and Ionis Pharmaceuticals; CHERISH ClinicalTrials.gov number, NCT02292537 .).
Magnetic Resonance Imaging Underestimation of Prostate Cancer Geometry: Use of Patient Specific Molds to Correlate Images with Whole Mount Pathology.

We evaluated the accuracy of magnetic resonance imaging in determining the size and shape of localized prostate cancer.



The subjects were 114 men who underwent multiparametric magnetic resonance imaging before radical prostatectomy with patient specific mold processing of the specimen from 2013 to 2015. T2-weighted images were used to contour the prostate capsule and cancer suspicious regions of interest. The contours were used to design and print 3-dimensional custom molds, which permitted alignment of excised prostates with magnetic resonance imaging scans. Tumors were reconstructed in 3 dimensions from digitized whole mount sections. Tumors were then matched with regions of interest and the relative geometries were compared.



Of the 222 tumors evident on whole mount sections 118 had been identified on magnetic resonance imaging. For the 118 regions of interest mean volume was 0.8 cc and the longest 3-dimensional diameter was 17 mm. However, for matched pathological tumors, of which most were Gleason score 3 + 4 or greater, mean volume was 2.5 cc and the longest 3-dimensional diameter was 28 mm. The median tumor had a 13.5 mm maximal extent beyond the magnetic resonance imaging contour and 80% of cancer volume from matched tumors was outside region of interest boundaries. Size estimation was most accurate in the axial plane and least accurate along the base-apex axis.



Magnetic resonance imaging consistently underestimates the size and extent of prostate tumors. Prostate cancer foci had an average diameter 11 mm longer and a volume 3 times greater than T2-weighted magnetic resonance imaging segmentations. These results may have important implications for the assessment and treatment of prostate cancer.
Multi-electrode array technologies for neuroscience and cardiology.

At present, the prime methodology for studying neuronal circuit-connectivity, physiology and pathology under in vitro or in vivo conditions is by using substrate-integrated microelectrode arrays. Although this methodology permits simultaneous, cell-non-invasive, long-term recordings of extracellular field potentials generated by action potentials, it is 'blind' to subthreshold synaptic potentials generated by single cells. On the other hand, intracellular recordings of the full electrophysiological repertoire (subthreshold synaptic potentials, membrane oscillations and action potentials) are, at present, obtained only by sharp or patch microelectrodes. These, however, are limited to single cells at a time and for short durations. Recently a number of laboratories began to merge the advantages of extracellular microelectrode arrays and intracellular microelectrodes. This Review describes the novel approaches, identifying their strengths and limitations from the point of view of the end users--with the intention to help steer the bioengineering efforts towards the needs of brain-circuit research.
Activation of gonadotropin-releasing hormone neurons by kisspeptin as a neuroendocrine switch for the onset of puberty.

We examined the role of kisspeptin and its receptor, the G-protein-coupled receptor GPR54, in governing the onset of puberty in the mouse. In the adult male and female mouse, kisspeptin (10-100 nM) evoked a remarkably potent, long-lasting depolarization of >90% of gonadotropin-releasing hormone (GnRH)-green fluorescent protein neurons in situ. In contrast, in juvenile [postnatal day 8 (P8) to P19] and prepubertal (P26-P33) male mice, kisspeptin activated only 27 and 44% of GnRH neurons, respectively. This developmental recruitment of GnRH neurons into a kisspeptin-responsive pool was paralleled by an increase in the ability of centrally administered kisspeptin to evoke luteinizing hormone secretion in vivo. To learn more about the mechanisms through which kisspeptin-GPR54 signaling at the GnRH neuron may change over postnatal development, we performed quantitative in situ hybridization for kisspeptin and GPR54 transcripts. Approximately 90% of GnRH neurons were found to express GPR54 mRNA in both juvenile and adult mice, without a detectable difference in the mRNA content between the age groups. In contrast, the expression of KiSS-1 mRNA increased dramatically across the transition from juvenile to adult life in the anteroventral periventricular nucleus (AVPV; p < 0.001). These results demonstrate that kisspeptin exerts a potent depolarizing effect on the excitability of almost all adult GnRH neurons and that the responsiveness of GnRH neurons to kisspeptin increases over postnatal development. Together, these observations suggest that activation of GnRH neurons by kisspeptin at puberty reflects a dual process involving an increase in kisspeptin input from the AVPV and a post-transcriptional change in GPR54 signaling within the GnRH neuron.
Video-based Framework for Safer and Smarter Computer Aided Surgery

The paramount emphasis on safety in surgical robotics has driven its incorporation at multiple levels within all stages of their development, testing, validation and deployment . Nonetheless, such systems finally have come back full-circle to fundamentally rely solely on the surgeon-in-the-loop to ensure safe operation amidst a host of real-world uncertainties and complexities. For example, the finite life and slack in the cables of the passive-robotic surgical instruments lead to toolpositioning inaccuracies, requiring surgeons to compensate for this error. We would like to make a case for taking advantage of the rich information content to create automated-assists that can help make robotic surgeries smarter and safer. Specifically, in this paper we seek to develop and implement real-time videobased understanding for improved situational awareness and context-based decision support in robotic surgeries.
Towards a Theoretical Understanding of Hashing-Based Neural Nets

Parameter reduction has been an important topic in deep learning due to the everincreasing size of deep neural network models and the need to train and run them on resource limited machines. Despite many efforts in this area, there were no rigorous theoretical guarantees on why existing neural net compression methods should work. In this paper, we provide provable guarantees on some hashing-based parameter reduction methods in neural nets. First, we introduce a neural net compression scheme based on random linear sketching (which is usually implemented efficiently via hashing), and show that the sketched (smaller) network is able to approximate the original network on all input data coming from any smooth and wellconditioned low-dimensional manifold. The sketched network can also be trained directly via back-propagation. Next, we study the previously proposed HashedNets architecture and show that the optimization landscape of one-hidden-layer HashedNets has a local strong convexity property similar to a normal fully connected neural network. We complement our theoretical results with empirical verifications.
Antibacterial activities of some Indian traditional plant extracts

Department of Microbiology, School of Life Sciences, Bharathidasan University, Tiruchirappali 620 024, Tamilnadu, India. Department of Medical Biotechnology, Sri Ramachandra University, Porur, Chennai 600 116, Tamilnadu, India. CAS Marine Biology, Annamalai University, Parangipettai 608 502, Tamilnadu, India. Department of Zoology, DDE, Annamalai University, Annamalai Nagar 608 002, Tamilnadu, India Asian Pacific Journal of Tropical Disease (2012)S291-S295
An Updated Model to Determine the Life Remaining of Transformer Insulation

In this paper, we present an update to the models used to calculate the remaining life of transformer paper insulation. A drawback to the current IEEE method is that it does not take into account the availability of water and oxygen on the life expectancy of Kraft paper insulation. We therefore set out to test an algorithm which does take these into account. For our investigation, we loaded three test transformers and ran them to near their end of life. Kinetic equations were applied to model the fall in the degree of polymerization of paper. Our model showed better agreement with the test results than that given from using the IEEE standard. The IEEE standard gives life expectancy for Kraft paper being aged in minimal oxygen under dry conditions, which is not necessarily representative of an old transformer. Wet paper and using high levels of oxygen can age nearly 40 times faster. The IEEE method needs to take the synergistic effect of water and oxygen on increasing the rate of paper aging into account.
Bird's eye view: Cooperative exploration by UGV and UAV

This paper proposes a solution to the problem of cooperative exploration using an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). More specifically, the UGV navigates through the free space, and the UAV provides enhanced situational awareness via its higher vantage point. The motivating application is search and rescue in a damaged building. A camera atop the UGV is used to track a fiducial tag on the underside of the UAV, allowing the UAV to maintain a fixed pose relative to the UGV. Furthermore, the UAV uses its front facing camera to provide a birds-eye-view to the remote operator, allowing for observation beyond obstacles that obscure the UGV's sensors. The proposed approach has been tested using a TurtleBot 2 equipped with a Hokuyo laser ranger finder and a Parrot Bebop 2. Experimental results demonstrate the feasibility of this approach. This work is based on several open source packages and the generated code is available on-line.
Outsourcing Software Testing: A Case Study in the Oulu Area

Global software engineering increases coordination, communication, and control challenges in software development. The testing phase in this context is not a widely researched subject. In this paper, we study the outsourcing of software testing in the Oulu area, research the ways in which it is used, and determine the observable benefits and obstacles. The companies that participated in this study were found to use the outsourcing possibility of software testing with good efficiency and their testing process was considered to be mature. The most common benefits, in addition to the companies' cost savings, included the utilization of time zone differences for around-the-clock productivity, a closer proximity to the market, an improved record of communication and the tools that record the audit materials. The most commonly realized difficulties consisted of teamwork challenges, a disparate tool infrastructure, tool expense, and often-elevated coordination costs. We utilized in our study two matrices that consist in one dimension of the three distances, control, coordination, and communication, and in another dimension of four distances, temporal, geographical, socio-cultural and technical. The technical distance was our extension to the matrix that has been used as the basis for many other studies about global software development and outsourcing efforts. Our observations justify the extension of matrices with respect to the technical distance.
Protecting Home User Devices with an SDN-Based Firewall

Internet-connected consumer electronics marketed as smart devices (also known as Internet-of-Things devices) usually lack essential security protection mechanisms. This puts user privacy and security in great danger. One of the essential steps to compromise vulnerable devices is locating them through horizontal port scans. In this paper, we focus on the problem of detecting horizontal port scans in home networks. We propose a software-defined networking (SDN)-based firewall platform that is capable of detecting horizontal port scans. Current SDN implementations (e.g., OpenFlow) do not provide access to packet-level information, which is essential for network security applications, due to performance limitations. Our platform uses FleXight, our proposed new information channel between SDN controller and data path elements to access packet-level information. FleXight uses per-flow sampling and dynamical sampling rate adjustments to provide the necessary information to the controller while keeping the overhead very low. We evaluate our solution on a large real-world packet trace from an ISP and show that our system can identify all attackers and 99% of susceptible victims with only 0.75% network overhead. We also present a detailed usability analysis of our system.
The Impact of Scaffolding and Student Achievement Levels in a Problem-based Learning Environment

This study examined how scaffolds and student achievement levels influence inquiry and performance in a problem-based learning environment. The scaffolds were embedded within a hypermedia program that placed students at the center of a problem in which they were trying to become the youngest person to fly around the world in a balloon. One-hundred and eleven seventh grade students enrolled in a science and technology course worked in collaborative groups for a duration of 3 weeks to complete a project that included designing a balloon and a travel plan. Student groups used one of three problem-based, hypermedia programs: (1) a no scaffolding condition that did not provide access to scaffolds, (2) a scaffolding optional condition that provided access to scaffolds, but gave students the choice of whether or not to use them, and (3) a scaffolding required condition required students to complete all available scaffolds. Results revealed that students in the scaffolding optional and scaffolding required conditions performed significantly better than students in the no scaffolding condition on one of the two components of the group project. Results also showed that student achievement levels were significantly related to individual posttest scores; higherachieving students scored better on the posttest than lower-achieving students. In addition, analyses of group notebooks confirmed qualitative differences between students in the various conditions. Specifically, those in the scaffolding required condition produced more highly organized project notebooks containing a higher percentage of entries directly relevant to the problem. These findings suggest that scaffolds may enhance inquiry and performance, especially when students are required to access and
[Wegener's granulomatosis with oral mucosal involvement as first manifestation].

Wegener’s granulomatosis (WG) is an autoimmune disease, which particularly affects the upper respiratory pathways, lungs and kidney. Oral mucosal involvement presents in around 5%--10% of cases and may be the first disease symptom. Predominant manifestation is granulomatous gingivitis erythematous papules; mucosal necrosis and non-specific ulcers with or without impact on adjacent structures. Clinically speaking, the most characteristic lesion presents as a gingival hyperplasia of the gum, with hyperaemia and petechias on its surface which bleed when touched. Due to its appearance, it has been called ‘‘Strawberry gingiva’’. The following is a clinical case in which the granulomatous strawberry gingivitis was the first sign of WG.
Insulin Glargine/Lixisenatide: A Review in Type 2 Diabetes

Subcutaneous insulin glargine/lixisenatide (Suliqua™) is a titratable, fixed-ratio combination of a long-acting basal insulin analogue and a glucagon-like peptide-1 (GLP-1) receptor agonist for the treatment of adult patients with inadequately controlled type 2 diabetes. Once-daily insulin glargine/lixisenatide, in combination with metformin, provided effective glycaemic control and was generally well tolerated in the 30-week, multinational, phase 3 LixiLan-O and LixiLan-L trials in insulin-naive and -experienced adult patients with inadequately controlled type 2 diabetes. Although long-term clinical experience with this fixed-ratio combination is currently lacking, given its convenient once-daily regimen and beneficial effects on glycaemic control and bodyweight loss in the absence of an increase in the incidence of hypoglycaemia, insulin glargine/lixisenatide is an emerging option for the treatment of adult patients with type 2 diabetes to improve glycaemic control when this has not been provided by metformin alone or metformin combined with another OAD or basal insulin.
Layer-Based Approach for Image Pair Fusion

Recently, image pairs, such as noisy and blurred images or infrared and noisy images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer, such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.
AN INTELLIGENT CONVERSATION AGENT FOR HEALTH CARE DOMAIN

Human Computer Interaction is one of the pervasive application areas of computer science to develop with multimodal interaction for information sharings. The conversation agent acts as the major core area for developing interfaces between a system and user with applied AI for proper responses. In this paper, the interactive system plays a vital role in improving knowledge in the domain of health through the intelligent interface between machine and human with text and speech. The primary aim is to enrich the knowledge and help the user in the domain of health using conversation agent to offer immediate response with human companion feel.