nils-herrmann/clean_scirepeval huggingface dataset:
NK Cells Preferentially Target Tumor Cells with a Cancer Stem Cell Phenotype
Venue: The Journal of Immunology
Year: 2015

Increasing evidence supports the hypothesis that cancer stem cells (CSCs) are resistant to antiproliferative therapies, able to repopulate tumor bulk, and seed metastasis. NK cells are able to target stem cells as shown by their ability to reject allogeneic hematopoietic stem cells but not solid tissue grafts. Using multiple preclinical models, including NK coculture (autologous and allogeneic) with multiple human cancer cell lines and dissociated primary cancer specimens and NK transfer in NSG mice harboring orthotopic pancreatic cancer xenografts, we assessed CSC viability, CSC frequency, expression of death receptor ligands, and tumor burden. We demonstrate that activated NK cells are capable of preferentially killing CSCs identified by multiple CSC markers (CD24+/CD44+ CD133+ and aldehyde dehydrogenasebright) from a wide variety of human cancer cell lines in vitro and dissociated primary cancer specimens ex vivo. We observed comparable effector function of allogeneic and autologous NK cells. We also observed preferential upregulation of NK activation ligands MICA/B, Fas, and DR5 on CSCs. Blocking studies further implicated an NKG2D dependent mechanism for NK killing of CSCs. Treatment of orthotopic human pancreatic cancer tumor bearing NSG mice with activated NK cells led to significant reductions in both intratumoral CSCs and tumor burden. Taken together, these data from multiple preclinical models, including a strong reliance on primary human cancer specimens, provide compelling preclinical evidence that activated NK cells preferentially target cancer cells with a CSC phenotype, highlighting the translational potential of NK immunotherapy as part of a combined modality approach for refractory solid malignancies.
McPoRA: A Multi chain Proof of Rapid Authentication for Post Blockchain Based Security in Large Scale Complex Cyber Physical Systems
Venue: 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Year: 2020

Internet of Things (IoT) research is in full swing to integrate recent technologies such as the blockchain to enhance system security. However, the blockchain faces many limitations, such as resource demands, energy requirements, scalability, and high latency. This article presents a novel post blockchain structure that integrates a multi blockchain in one ledger using a directed acyclic graph (DAG) structure called a multi chain. The multi chain structure resolves the issues of scalability and storage and is a candidate to replace the traditional blockchain in IoT applications. This article also introduces a new consensus algorithm called "Multi Chain Proof of Rapid Authentication" (McPoRA) to improve latency, which is a crucial factor in IoT resource constrained devices. McPoRA is approx. 4000X faster than proof of work (PoW) and 55X faster than Proof of Authentication (PoAh)
CTRec: A Long Short Demands Evolution Model for Continuous Time Recommendation
Venue: SIGIR
Year: 2019

In e commerce, users' demands are not only conditioned by their profile and preferences, but also by their recent purchases that may generate new demands, as well as periodical demands that depend on purchases made some time ago. We call them respectively short term demands and long term demands. In this paper, we propose a novel self attentive Continuous Time Recommendation model (CTRec) for capturing the evolving demands of users over time. For modeling such time sensitive demands, a Demand aware Hawkes Process (DHP) framework is designed in CTRec to learn from the discrete purchase records of users. More specifically, a convolutional neural network is utilized to capture the short term demands; and a self attention mechanism is employed to capture the periodical purchase cycles of long term demands. All types of demands are fused in DHP to make final continuous time recommendations. We conduct extensive experiments on four real world commercial datasets to demonstrate that CTRec is effective for general sequential recommendation problems, including next item and next session/basket recommendations. We observe in particular that CTRec is capable of learning the purchase cycles of products and estimating the purchase time of a product given a user.
Multiple Alignment of Promoter Sequences from the Arabidopsis thaliana L. Genome
Venue: Genes
Year: 2021

In this study, we developed a new mathematical method for performing multiple alignment of highly divergent sequences (MAHDS) i.e. sequences that have on average more than 2.5 substitutions per position (x) We generated sets of artificial DNA sequences with x ranging from 0 to 4.4 and applied MAHDS as well as currently used multiple sequence alignment algorithms, including ClustalW, MAFFT, T Coffee, Kalign, and Muscle to these sets. The results indicated that most of the existing methods could produce statistically significant alignments only for the sets with x 2.5, whereas MAHDS could operate on sequences with x 4.4. We also used MAHDS to analyze a set of promoter sequences from the Arabidopsis thaliana genome and discovered many conserved regions upstream of the transcription initiation site (from 499 to +1 bp) a part of the downstream region (from +1 to +70 bp) also significantly contributed to the obtained alignments. The possibilities of applying the newly developed method for the identification of promoter sequences in any genome are discussed. A server for multiple alignment of nucleotide sequences has been created.
Technological advancements and error rates in radiation therapy delivery.
Venue: International journal of radiation oncology, biology, physics
Year: 2011

Technological advances in radiation therapy (RT) delivery have the potential to reduce errors via increased automation and built in quality assurance (QA) safeguards, yet may also introduce new types of errors. Intensity modulated RT (IMRT) is an increasingly used technology that is more technically complex than three dimensional (3D) conformal RT and conventional RT. We determined the rate of reported errors in RT delivery among IMRT and 3D/conventional RT treatments and characterized the errors associated with the respective techniques to improve existing QA processes.  All errors in external beam RT delivery were prospectively recorded via a nonpunitive error reporting system at Brigham Women's Hospital/Dana Farber Cancer Institute. Errors are defined as any unplanned deviation from the intended RT treatment and are reviewed during monthly departmental quality improvement meetings. We analyzed all reported errors since the routine use of IMRT in our department, from January 2004 to July 2009. Fisher's exact test was used to determine the association between treatment technique (IMRT vs. 3D/conventional) and specific error types. Effect estimates were computed using logistic regression.  There were 155 errors in RT delivery among 241,546 fractions (0.06% and none were clinically significant. IMRT was commonly associated with errors in machine parameters (nine of 19 errors) and data entry and interpretation (six of 19 errors) IMRT was associated with a lower rate of reported errors compared with 3D/conventional RT (0.03% vs. 0.07% p 0.001) and specifically fewer accessory errors (odds ratio, 0.11; 95% confidence interval, 0.01 0.78) and setup errors (odds ratio, 0.24; 95% confidence interval, 0.08 0.79)  The rate of errors in RT delivery is low. The types of errors differ significantly between IMRT and 3D/conventional RT, suggesting that QA processes must be uniquely adapted for each technique. There was a lower error rate with IMRT compared with 3D/conventional RT, highlighting the need for sustained vigilance against errors common to more traditional treatment techniques.
Complete mitochondrial genome of gray black carp (Mylopharyngodon piceus)
Venue: Mitochondrial DNA. Part B, Resources
Year: 2020

The black carp (Mylopharyngodon piceus) native to eastern Asian, is a large, commercially valuable fish, and has been widely introduced to other countries. In this study, the mitochondrial gene sequence of gray black carp (M. piceus MT084757) in Foshan, Guangdong Province was first determined using the Sanger sequencing method. The mitochondrial DNA genome was 16,616 bp in length, including 13 protein coding genes (PCGs) two rRNA genes, 22 transfer RNA genes, and a non coding control region (D loop) The overall nucleotide composition of the mitochondrial DNA is 32.04% A, 24.52% T, 15.68% C, 27.76% G, with 56.56% AT, respectively. Phylogenetic tree analysis suggests that the gray black carp (M. piceus MT 084757) is closely related to Elopichthys bambus and Squaliobarbus curriculus. The complete mitochondrial genome of the gray black carp (M. piceus MT 084757) would be useful for researching the causes of changes in body color.
Noise Flow: Noise Modeling With Conditional Normalizing Flows
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019

Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long standing additive white Gaussian and heteroscedastic (signal dependent) noise models widely used in the literature provide only a coarse approximation of real sensor noise. This paper introduces Noise Flow, a powerful and accurate noise model based on recent normalizing flow architectures. Noise Flow combines well established basic parametric noise models (e.g. signal dependent noise) with the flexibility and expressiveness of normalizing flow networks. The result is a single, comprehensive, compact noise model containing fewer than 2500 parameters yet able to represent multiple cameras and gain factors. Noise Flow dramatically outperforms existing noise models, with 0.42 nats/pixel improvement over the camera calibrated noise level functions, which translates to 52% improvement in the likelihood of sampled noise. Noise Flow represents the first serious attempt to go beyond simple parametric models to one that leverages the power of deep learning and data driven noise distributions.
Effect of Limosilactobacillus reuteri LRE02 Lacticaseibacillus rhamnosus LR04 Combination on Antibiotic Associated Diarrhea in a Pediatric Population: A National Survey
Venue: Journal of clinical medicine
Year: 2020

Probiotics are living microorganisms, which, upon oral ingestion, may prevent antibiotic associated diarrhea (AAD) through the normalization of an unbalanced gastrointestinal flora. The objective of this study was to evaluate the benefits of a probiotic combination (Limosilactibacillus reuteri LRE02 DSM 23878 and Lacticaseibacillus rhamnosus LR04 DSM 16605) on the prevention of AAD in an outpatient pediatric setting. Questionnaires were delivered to pediatricians by each patient/parent during the visits after antibiotics and probiotics treatment to monitor physiological parameters. The primary outcome of both groups (probiotics and no probiotics treated) was the evaluation of the prevalence of AAD between the two groups. Evaluation of stool consistency using the Bristol Stool Scale (BSS) score was performed, as well as the evaluation of AAD duration, frequencies of daily evacuation, and the beginning of diarrhea and weight loss during AAD in both groups and related to antibiotic categories. Results indicated that probiotics, at the recommended dosage of 1.2 x 109 CFU (Colony Forming Unit) per day for 30 days, are associated with lower rates of AAD and a decreased number of days with diarrhea, independent of the type of antibiotic used. Moreover, the use of probiotics resulted in a normal stool consistency in a shorter time period, as evaluated by the BSS.
Strong Optimal Classification Trees
Venue: ArXiv
Year: 2021

Decision trees are among the most popular machine learning models and are used routinely in applications ranging from revenue management and medicine to bioinformatics. In this paper, we consider the problem of learning optimal binary classification trees. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heuristic approaches and the tremendous improvements in mixedinteger optimization (MIO) technology. Yet, existing MIO based approaches from the literature do not leverage the power of MIO to its full extent: they rely on weak formulations, resulting in slow convergence and large optimality gaps. To fill this gap in the literature, we propose an intuitive flow based MIO formulation for learning optimal binary classification trees. Our formulation can accommodate side constraints to enable the design of interpretable and fair decision trees. Moreover, we show that our formulation has a stronger linear optimization relaxation than existing methods. We exploit the decomposable structure of our formulation and max flow/min cut duality to derive a Benders' decomposition method to speed up computation. We propose a tailored procedure for solving each decomposed subproblem that provably generates facets of the feasible set of the MIO as constraints to add to the main problem. We conduct extensive computational experiments on standard benchmark datasets on which we show that our proposed approaches are 31 times faster than state of the art MIO based techniques and improve out of sample performance by up to 8%
Image Restoration Using Joint Statistical Modelling In Space Transform Domain
Year: 2015

This paper presents a completely unique strategy for accurate image restoration by characterizing each native smoothness and nonlocal self similarity of natural pictures during a unified statistical manner. the most contributions area unit three fold. First, from the angle of image statistics, a joint statistical modeling (JSM) in an accommodative hybrid space transform domain is established, that offers a strong} mechanism of mixing native smoothness and nonlocal self similarity at the same time to make sure a a lot of reliable and robust estimation. Second, a replacement variety of minimization purposeful for finding the image inverse drawback is developed using JSM underneath a regularization based framework. Finally, so as to form JSM tractable and robust, a replacement Split Bregman based algorithmic program is developed to expeditiously solve the on top of severely underdetermined inverse drawback associated with theoretical proof of convergence. intensive experiments on image inpainting, image deburring, and mixed mathematician and salt and pepper noise removal applications verify the effectiveness of the proposed algorithmic program.
Characterization of the Fecal Bacterial Microbiota of Healthy and Diarrheic Dairy Calves
Venue: Journal of veterinary internal medicine
Year: 2017

Neonatal diarrhea accounts for more than 50% of total deaths in dairy calves. Few population based studies of cattle have investigated how the microbiota is impacted during diarrhea.  To characterize the fecal microbiota and predict the functional potential of the microbial communities in healthy and diarrheic calves.  Fifteen diarrheic calves between the ages of 1 and 30 days and 15 age matched healthy control calves were enrolled from 2 dairy farms. The Illumina MiSeq sequencer was used for high throughput sequencing of the V4 region of the 16S rRNA gene (Illumina, San Diego, CA) Results Significant differences in community membership and structure were identified among healthy calves from different farms. Differences in community membership and structure also were identified between healthy and diarrheic calves within each farm. Based on linear discriminant analysis effect size (LEfSe) the genera Bifidobacterium, Megamonas, and a genus of the family Bifidobacteriaceae were associated with health at farm 1, whereas Lachnospiraceae incertae sedis, Dietzia and an unclassified genus of the family Veillonellaceae were significantly associated with health at farm 2. The Phylogenetic Investigation of Communities Reconstruction of Unobserved States (PICRUSt) analysis indicated that diarrheic calves had decreased abundances of genes responsible for metabolism of various vitamins, amino acids, and carbohydrate.  The fecal microbiota of healthy dairy calves appeared to be farm specific as were the changes observed during diarrhea. The differences in microbiota structure and membership between healthy and diarrheic calves suggest that dysbiosis can occur in diarrheic calves and it is associated with changes in predictive metagenomic function.
A ansiedade e o distanciamento familiar em estudantes universitarios
Year: 2020

Analisar a correlacao entre ansiedade e o distanciamento familiar em estudantes da Universidade Federal de Sergipe (UFS) campus Lagarto.  Trata se de uma pesquisa descritiva e quantitativa realizada com discentes da Universidade Federal de Sergipe campus de Lagarto. A pesquisa foi realizada nos meses de junho e julho de 2019, em que se utilizou o Inventario de Ansiedade de Beck (BAI) para medir a intensidade dos sintomas da ansiedade por meio do auto relato.  Dados de 145 universitarios foram coletados. Desses, 62,8% eram do sexo feminino, 94,5% tinham entre 18 e 21 anos de idade, 69% eram solteiros (as) 95,2% nao possuiam vinculo empregaticio, 60,7% moravam na cidade onde a universidade se localiza, 58,6% eram sedentarios, 81,4% dormiam menos que 7 horas, 11,7% possuiam diagnostico de doenca mental, 76,6% nao realizavam tratamento psicologico e 25,5% usavam medicamentos para a ansiedade. Houve homogeneidade na quantidade de casos para a ansiedade leve, moderada e grave, com menores casos de ansiedade minima, sendo que esses niveis se relacionaram significativamente apenas com realizar atividade fisica (0,019) e tratamento com psicologo (0,045) Conclusao: Ha alta prevalencia de ansiedade entre os universitarios estudados, porem esse fato nao esta relacionado ao distanciamento familiar.
Efficacy of fungicides, and resistance among cultivars, in the control of strawberry powdery mildew
Venue: Annual Report of The Kansai Plant Protection Society
Year: 2019

We investigated the efficacy of fungicides and differences among cultivars in resistance against strawberry powdery mildew. Ten fungicides were applied to strawberry plants, on three occasions, at seven days intervals. Relative to the untreated plants, disease severity was significantly reduced by eight of the fungicides after the second application, and by ten of the fungicides after the third application. Of the seven strawberry cultivars tested, the three with the highest disease resistance, from most to least resistant, were Yumenoka, Kaorino, and Akihime. These results indicate that multiple applications of fungicides and the use of resistant cultivars can effectively control strawberry powdery mildew.
Fire and Gun Violence based Anomaly Detection System Using Deep Neural Networks
Venue: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC)
Year: 2020

Real time object detection to improve surveillance methods is one of the sought after applications of Convolutional Neural Networks (CNNs) This research work has approached the detection of fire and handguns in areas monitored by cameras. Home fires, industrial explosions, and wildfires are a huge problem that cause adverse effects on the environment. Gun violence and mass shootings are also on the rise in certain parts of the world. Such incidents are time sensitive and can cause a huge loss to life and property. Hence, the proposed work has built a deep learning model based on the YOLOv3 algorithm that processes a video frame by frame to detect such anomalies in real time and generate an alert for the concerned authorities. The final model has a validation loss of 0.2864, with a detection rate of 45 frames per second and has been benchmarked on datasets like IMFDB, UGR, and FireNet with accuracies of 89.3% 82.6% and 86.5% respectively. Experimental result satisfies the goal of the proposed model and also shows a fast detection rate that can be deployed indoor as well as outdoors.
J Ewish I Ntragroup C Onvergence and D Ivergence in the U Niversity H Ebrew C Lass
Year: 2009

A S A H EBREW LANGUAGE TEACHER and language acquisition researcher living in North America, I have found myself thinking deeply about how my commitment to the Hebrew language affects and creates my sense of Jewish identity. I spent time as a young child in Israel, where I was immersed in the Hebrew language and Israeli culture. When I eventually returned to Canada with my family, the ability to communicatively function in modern Hebrew marked me as different from my peers. Using a qualitative, ethnographic methodological framework based in emergent, grounded theory (Glaser and Strauss, 1967) I investigated the attitudes and feelings of individuals who participated in an advanced, undergraduate modern Hebrew class at a large Canadian university. 1 How did others living in the Canadian Jewish Diaspora negotiate their dual linguistic, national, and cultural loyalties? I entered the research field with three key questions. First, what framework of ethnic identity did participants structure in their lives? Rather than define identity specifically as Jewish identity, I chose the term "ethnic identity" to ensure freedom and flexibility in defining senses of self while limiting the subject area to the notion of shared ancestry, lineage, or heritage. Second, I asked how Hebrew language fit into participants' frameworks of ethnic identity. Because the students, professor, and I committed time and effort to the study, teaching, or practice of the language, it clearly held a place of priority in our lives, and, I hypothesized, our ethnic identities. Third, I wondered
Photo Composition Feedback and Enhancement Exploiting Spatial Design Categories and the Notan Dark Light Principle
Venue: Mobile Cloud Visual Media Computing
Year: 2015

In this chapter, we present techniques to provide composition feedback and enhancement for photographs. In order to suit mobile applications, we have designed systems requiring minimal input from the users. The essence of composition is to create unity in a picture, which includes the balance of visual elements from many aspects. We hereby explore several fundamental concepts in composition and develop our new methods accordingly. Albeit much exploited by artists, these concepts have barely crossed over to multimedia or computer vision research. First, we have developed a tool to categorize images by spatial design into diagonal, horizontal, vertical, and centered composition types. Composition in this regard is known to be well associated with aesthetics and emotional response. For instance, placing visual elements diagonally creates a sense of movement; and horizontal placement tends to convey tranquility. This composition analysis tool enables the retrieval of highly aesthetic exemplar images from the corpus which are similar in content and composition to the snapshot. Second, the arrangement of dark and light masses in a picture, referred to as Notan in visual art, is a crucial factor in composition. We propose an approach to adjust the tonal values in an image, targeting directly at achieving an aesthetically more appealing Notan. This method addresses composition enhancement from a high level of spatial arrangement, a remarkable difference from improving relatively low level characteristics such as contrast and dynamic ranges.