Miracle-dz/UniFAR-FTUs huggingface dataset:
Sfi1p has multiple roles in the spindle pole body cycle

In order to identify new mitotic defects, a screen for mutants synthetic lethal with a madlA deletion strain was performed in S. cerevisiae. Mad ip is a component of the spindle assembly checkpoint. Four of the mutants isolated in the screen were novel mutations in the essential sf11 gene. In this study, Sfilp is shown to be a spindle pole body (SPB) protein that contains a conserved set of 21 amino acid repeats. Its phosphorylation is cell cycle regulated, with most phosphorylation being present in an alpha factor (GO/Gi) arrest, the time of spindle pole body duplication. The mutants from the screen all had mutations C-terminal to the conserved 21 amino acid repeats. This C terminal region is not conserved outwith the budding yeast, but within the budding yeasts family is the most conserved region. Deletion of the C terminal region is lethal, and results in mis-localisation of the protein. Complementary biochemical and 2-hybrid methods were used to search for binding partners of the C-terminal region but no significant results were obtained. The sf11-CT mutants are lethal in combination with a range mutants in spindle checkpoint proteins, indicating that the defect is recognised by the spindle assembly checkpoint. The mutants are all temperature sensitive and benomyl resistant, and showed elevated levels of chromosome loss. The mutants showed weak genetic interactions with a selection of other spindle pole body genes (BBP1, IVIPS2, NDC1) and a microtubule motor (CIN8). At all temperatures the sf11 -CT mutants showed varying levels of large budded cells delayed in mitosis with spindle pole bodies very close together (0.2-0.4 tim) and few normal metaphase (1-1.5 .tm) spindles. Examination of these abnormal SPB pairs by electron microscopy revealed many large budded cells containing sets of paired spindle pole bodies still attached by a half bridge. In these cases, both spindle pole
Delineation of Nitrate Reduction Hotspots in Artificially Drained Areas through Assessment of Small-Scale Spatial Variability of Electrical Conductivity Data

Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3−) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale.
Study of Damage Quantification of Concrete Drainage Pipes Based on Point Cloud Segmentation and Reconstruction

The urban drainage system is an important part of the urban water cycle. However, with the aging of drainage pipelines and other external reasons, damages such as cracks, corrosion, and deformation of underground pipelines can cause serious consequences such as urban waterlogging and road collapse. At present, the detection of underground drainage pipelines mostly focuses on the qualitative identification of pipeline damage, and it is impossible to quantitatively analyze pipeline damage. Therefore, a method to quantify the damage volume of concrete pipes that combines surface segmentation and reconstruction is proposed. An RGB-D sensor is used to collect the damage information of the drainage pipeline, and the collected depth frame is registered to generate the pipeline’s surface point cloud. Voxel sampling and Gaussian filtering are used to improve data processing efficiency and reduce noise, respectively, and the RANSAC algorithm is used to remove the pipeline’s surface information. The ball-pivoting algorithm is used to reconstruct the surface of the segmented damage data and pipe’s surface information, and finally to obtain the damage volume. In order to evaluate, we conducted our research on real-world materials. The measurement results show that the method proposed in this paper measures an average relative error of 7.17% for the external damage volume of concrete pipes and an average relative error of 5.22% for the internal damage measurements of concrete pipes.
Study Based on Stackelberg Game about the Information Sharing Coordination in Supply Chain

This paper puts forward a two-echelon model with a supplier and a retailer and supposes the demand information is stochastic.Three situations are considered:(1)What are the conditions where the retailer likes to get this stochastic demand information in? (2) What are the requirements for retailer to share this information to its supplier? (3)How to coordinate between the supplier and the retailer? Study of these enables us to deeply understand the information sharing coordination and can give some insights for the practitioners in this area.
The Unfinished Agenda:
 Restoring Public Sector Bank Health in India

Good evening, friends. I am grateful to the Indian Institute of Banking and Finance (IIBF) for inviting me to deliver the 8 R K Talwar Memorial Lecture. Every institution must remember, venerate and celebrate the immense contributions of those who helped lay down and solidify its character for future generations to build upon. Principles, careers and lives such as those of Mr Talwar inspire us, as in Henry Wadsworth Longfellow’s The Psalm of Life:
Minimizing the Silver Free Ion Content in Starch Coated Silver Nanoparticle Suspensions with Exchange Cationic Resins

This work describes the optimization of a methodology for the reduction of silver ions from silver nanoparticle suspensions obtained from low-yield laboratory procedures. The laboratory synthesis of silver nanoparticles following a bottom-up approach starting from silver nitrate, originates silver ions that were not reduced to their fundamental state for nanoparticles creation at the end of the process. However, it is well known that silver ions can easily influence chemical assays due to their chemical reactivity properties and can limit biological assays since they interfere with several biological processes, namely intracellular ones, leading to the death of living cells or organisms. As such, the presence of silver ions is highly undesirable when conducting biological assays to evaluate the influence of silver nanoparticles. We report the development of an easy, low-cost, and rapid methodology that is based on cation exchange resins to minimize the silver ion content in a raw suspension of silver nanoparticles while preserving the integrity of the nanomaterials. This procedure preserves the physical-chemical properties of the nanoparticles, thus allowing the purified nanoparticulate systems to be biologically tested. Different types of cationic resins were tested, and the developed methodology was optimized by changing several parameters. A reduction from 92% to 10% of free silver/total silver ratio was achieved when using the Bio-Rad 50W-X8 100–200 mesh resin and a contact time of 15 min. Filtration by vacuum was used to separate the used resin from the nanoparticles suspension, allowing it to be further reused, as well as the purified AgNPs suspension.
The association between triglycerides/high-density lipoprotein cholesterol ratio, insulin resistance, and serum androgen levels in patients with polycystic ovary syndrome

Objectives: Insulin resistance and dyslipidemia are common comorbidities of polycystic ovary syndrome. We aim to evaluate the association between triglycerides/high-density lipoprotein cholesterol ratio, insulin resistance, and serum androgen levels in patients with polycystic ovary syndrome. Methods: We enrolled a total of 40 polycystic ovary syndrome patients and 20 healthy control subjects in this cross-sectional study. The polycystic ovary syndrome patients were divided into two subgroups obese and nonobese. The triglycerides/high-density lipoprotein cholesterol ratio and homeostatic model assessment of insulin resistance were calculated for all individuals. Demographic characteristics, serum levels of metabolic parameters, and androgens were compared between the study subgroups. P value < 0.05 was accepted as statistically significant. Results: The triglycerides/high-density lipoprotein cholesterol ratio was higher in obese polycystic ovary syndrome patients than in other groups (3.64 ± 3.06 vs. 1.07 ± 0.36 in control and 1.18 ± 0.53 in non-obese group, p < 0.005). The triglycerides/high-density lipoprotein cholesterol ratio was positively correlated with homeostatic model assessment of insulin resistance (r = 0.546, p < 0.001), total testosterone (r = 0.402, p = 0.010), and free androgen index (r = 0.609, p < 0.001) while was negatively correlated with sex hormonebinding globulin (r = -0.497, p = 0.001). Obese polycystic ovary syndrome patients had higher serum total testosterone levels, higher free androgen index, and lower sex hormone-binding globulin than non-obese polycystic ovary syndrome patients (0.71 ± 0.49 ng/mL vs. 0.45 ± 0.16 ng/mL, p = 0.006; 3.1 ± 1.91 vs. 1.01 ± 0.49, p < 0.005; and 26 ± 10.3 nmol/L vs. 59.6 ± 43.7 nmol/L, p < 0.005, respectively). Obese polycystic ovary syndrome patients had also worse lipid parameters, including high triglycerides and low high-density cholesterol when compared with other groups. Conclusions: The triglycerides/high-density lipoprotein cholesterol ratio was correlated with the homeostatic model assessment of insulin resistance, and androgenic hormonal profiles including total testosterone and free androgen index in patients with polycystic ovary syndrome.
Response Generation by Jointly Modeling Personalized Linguistic Styles and Emotions

Natural language generation (NLG) has been an essential technique for various applications, like XiaoIce and Siri, and engaged increasing attention recently. To improve the user experience, several emotion-aware NLG methods have been developed to generate responses coherent with a pre-designated emotion (e.g., the positive or negative). Nevertheless, existing methods cannot generate personalized responses as they frequently overlook the personalized linguistic style. Apparently, different human responsers tend to have different linguistic styles. Inspired by this, in this work, we focus on a novel research theme of personalized emotion-aware NLG (PENLG), whereby the generated responses should be coherent with the linguistic style of a pre-designated responser and emotion. In particular, we study PENLG under a scenario of generating personalized emotion-aware response for social media post. Yet it faces certain research challenges: (1) the user linguistic styles are implicit and complex by nature, and hence it is hard to learn their representations; and (2) linguistic styles and emotions are usually expressed in different manners in a response, and thus how to convey them properly in the generated responses is not easy. Toward this end, we present a novel scheme of PENLG, named CRobot, which consists of a personalized emotion-aware response generator and two discriminators, i.e., general discriminator and personalized emotion-aware discriminator. To be more specific, the post-based and avatar-based user linguistic style modeling methods are incorporated into the encoder-decoder–based generator, while the discriminators are devised to ensure that the generated response is fluent and consistent with both the emotion and the linguistic style of the user. Different from the traditional adversarial networks, we embed adversarial learning under the umbrella of reinforcement learning. In this way, the response generation problem can be tackled by the generator taking a sequence of actions on selecting the proper word of each timestep for output. To justify our model, we construct a large-scale response generation dataset based on Twitter, consisting of 6,763 tweets with a corresponding 1,461,713 response created by 153,664 users. Extensive experiments demonstrate that CRobot surpasses the state-of-the-art baselines regarding both subjective and objective evaluation.
Analysis of Railway Accident Causes and Risk

For the sake of providing a theoretical basis for improving railway safety and inducing risk,the railway safety influence factors were analyzed comprehensively and systematically.According to the related statistics of American railway accidents in 2005,the gray system theory was adopted to analyze the railway accident causes,and the primary and secondary safety factors in different types of incidents were identified.Based on the monte carlo analysis method,the fitting analysis of risk probabilities for all the influence factors was carried out by @Risk software from the point of view man-machine-circumstance and time factors.The results show that operation accidents are mainly caused by man-machine factors.
Genetic insights and evaluation of forensic features in Mongolian and Ewenki groups using the InDel variations.

BACKGROUND InDel polymorphisms show great potential for use with challenging DNA samples in forensic practice due to having similar advantages to STRs and SNPs. Large-scale InDel genotype data are becoming available world wide populations, thus providing an alternative for investigating genetic architectures in rarely studied populations from a genome perspective. METHODS Here, we genotyped 47 highly polymorphic InDel variations in 157 Mongolian and 100 Ewenki individuals from the Inner Mongolia Autonomous Region of China in order to evaluate their utility for forensic purposes. RESULTS The CDPs of the 47 InDels for these groups were calculated to be 0.999999999999999999874 and 0.999999999999999999677, respectively, while the CPEs were 0.99981 and 0.99975, respectively. The 47 InDel variations were therefore an efficient tool for forensic personal identification in the Mongolian and Ewenki ethnic groups. Comparison of results from the present study with datasets from previously published literatures and from the 1000 Genomes Project revealed a prominent East Asian ancestry component in the gene pool of both ethnic groups, hinting at the close genetic relationships between Mongolian, Ewenki and most East Asian populations. Furthermore, Han populations from northern China showed even closer genetic affinities with the Mongolian and Ewenki groups. CONCLUSIONS The data presented here would facilitate the forensic application of InDels for Mongolian and Ewenki ethnic minorities and expand our knowledge regarding the genetic diversities of these populations.
Addressing the Challenges of Last-mile: The Drone Routing Problem with Shared Fulfillment Centers

With the easing of restrictions worldwide, drones will become a preferred transportation mode for last-mile deliveries in the coming years. Drones offer, in fact, an optimal solution for many challenges faced with last-mile delivery as congestion and emissions and can streamline the last leg of the supply chain. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision, among which the limited drone range and payload. To overcome this issue, big companies such as Amazon, are already filing up patents for the development of fulfilment centers where drones can be restocked before flying out again for another delivery, effectively extending their range. Only a few authors have addressed the joint problem of operating these facilities and providing services to retail companies. This paper addresses this problem and proposes a mathematical formulation to show the viability of the proposed approach.
Experimenting Machine-Learning Algorithms for Morphological Disambiguation of Arabic Texts

Arabic language is characterized by its complexity and its morphological and orthographic variations including syntactic and semantic diversity of a word. This specificity may cause Arabic morphological ambiguity. We present in this paper a new architecture for morphological disambiguation of Arabic texts. The latter can be treated as a classification problem where the set of morphological features’ values represent classes, and a classification algorithm is used to assign a class to each word’s occurrence based on the context. The first step consists of identifying the correct morphological analysis of a non-vocalized Arabic word using the morphological dependencies extracted from the corpus of vocalized texts. Then, we propose a method of transforming imperfect training datasets into perfect data having precise attributes and certain classes. We experiment this architecture on a set of machine-learning classifiers using a corpus of classic Arabic texts. Results highlight some statistically significant improvement of SVM and Naïve Bayes classifiers in terms of disambiguation rate.
Solver-based Approaches for Robust Multi-index Selection Problems with Stochastic Workloads and Reconfiguration Costs

: Fast processing of database queries is a primary goal of database systems. Indexes are a crucial means for the physical design to reduce the execution times of database queries significantly. Therefore, it is of great interest to determine an efficient selection of indexes for a database management system (DBMS). However, as indexes cause additional memory consumption and the storage capacity of databases is limited, index selection problems are highly challenging. In this paper, we consider a basic index selection problem and address additional features, such as (i) multiple potential workloads, (ii) different risk-averse objectives, (iii) multi-index configurations, and (iv) reconfiguration costs. For the different problem extensions, we propose specific model formulations, which can be solved efficiently using solver-based solution techniques. The applicability of our concepts is demonstrated using reproducible synthetic datasets.
The application of remote video surveillance system in electric power system

This paper summarizes remote video surveillance system's present situation and direction of development. Technology and standard involved in the system are analyzed and discussed, especially recommend a new design idea. In narrow system,a design scheme based on PSTN and GSM network is proposed; in broadband system,recent development of video surveillance system is discussed,by combining network multimedia technology. Based the history and present situation of the application in electric power system,combined with the development in the near future,a design scheme of district video platform and substation's remote video surveillance system is proposed.
Detecting Narcissist Dark Triad Psychological Traits from Twitter

The fundamental basis of human behavior is personality. This human characteristic influences the tastes and preferences of individuals and the way they interact and communicate with each other. Nowadays, individuals express their feelings, opinions, and ideas to the world by using digital communication platforms such as social media. In recent years, several studies have proposed different models that apply Artificial Intelligence techniques to identify personality traits based mainly on the Big 5 Personality Model and the three subpersonalities of the Dark Triad through the linguistic analysis of their comments online. In this work, we present a study about identifying narcissist dark triad psychological traits. We propose two Machine Learning models to analyze users’ behavior in social media. Concretely, we develop a Support Vector Machine and Naïve Bayes method to classify the comments as having non-narcissist or narcissist traits. To train and test the developed method, we have employed NLP techniques to process comments from Twitter and created a manual dataset. Three different techniques have been designed and applied to label each tweet and comment. Then, we conducted several evaluations in which both models reached promising results.
An Overview of Potential Seaweed-Derived Bioactive Compounds for Pharmaceutical Applications

Nowadays, seaweeds are widely involved in biotechnological applications. Due to the variety of bioactive compounds in their composition, species of phylum Ochrophyta, class Phaeophyceae, phylum Rhodophyta and Chlorophyta are valuable for the food, cosmetic, pharmaceutical and nutraceutical industries. Seaweeds have been consumed as whole food since ancient times and used to treat several diseases, even though the mechanisms of action were unknown. During the last decades, research has demonstrated that those unique compounds express beneficial properties for human health. Each compound has peculiar properties (e.g., antioxidant, antimicrobial, antiviral activities, etc.) that can be exploited to enhance human health. Seaweed’s extracted polysaccharides are already involved in the pharmaceutical industry, with the aim of replacing synthetic compounds with components of natural origin. This review aims at a better understanding of the recent uses of algae in drug development, with the scope of replacing synthetic compounds and the multiple biotechnological applications that make up seaweed’s potential in industrial companies. Further research is needed to better understand the mechanisms of action of seaweed’s compounds and to embrace the use of seaweeds in pharmaceutical companies and other applications, with the final scope being to produce sustainable and healthier products.
Optimal Time to Replace Switchgrass Stand with a More Productive Variety

The net present value (NPV) criterion was employed to develop an economic model that can be used to determine if and when it is optimal to replace an existing switchgrass stand with an alternative higher biomass yielding cultivar. Yield data from a randomized field experiment conducted from 1994 to 2000 in Haskell and Chickasha, Oklahoma were used to represent average yields for the defender. The study assumed a constant and a declining future yield scenario for the defender. Under the assumptions used in the study, it was optimal to renovate a switchgrass stand if the improved cultivar produced a higher biomass yield of 16% or more for a 20 year planning horizon. The estimated breakeven NPV of the defender and challenger was $229 ha-1. For a plot with declining yields, the defender would have to be retained for about six years before being optimally replaced by a 10% yield increment challenger whereas a higher biomass yielding challenger would replace the defender immediately.
”Det er sjovt, hvor forskellige man er, selvom man er ens med sin skilsmisse” – En undersøgelse af børns oplevelser med samtalegrupper for skilsmissebørn

I denne artikel undersøger jeg børn med skilte forældres erfaringer med at deltage i en samtalegruppe. Jeg kigger nærmere på variationerne i børnenes forskellige måder at tale om deres liv med skilsmissen. Gennem et etnografisk inspireret feltstudie, der bygger på 23 børneinterviews samt seks måneders observationer i fire samtalegrupper på Sjælland i Danmark, udforsker jeg, hvordan børn i skilsmisse skaber mening i deres deltagelse i samtalegrupperne. Herunder, hvordan børns forskelligheder bliver opfattet af de professionelle, og på hvilke måder der skabes mulighed for at udfordre de dominerende forståelser af, hvad det vil sige at leve et hverdagsliv med skilsmisse. I artiklen træder det tydeligt frem, at børns generelle skilsmisseoplevelser varierer bredt og får forskellige implikationer for deres deltagelsesmuligheder. Nøgleord: Børn med skilte forældre, skilsmisse, samtalegrupper, børns meningsskabelse, sociale repræsentationer