Promoting Cold-Start Items in Recommender Systems

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users'' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.     

Information Filtering via Heterogeneous Diffusion in Online Bipartite Networks

The rapid expansion of Internet brings us overwhelming online information, which is impossible for an individual to go through all of it. Therefore, recommender systems were created to help people dig through this abundance of information. In networks composed by users and objects, recommender algorithms based on diffusion have been proven to be one of the best performing methods. Previous works considered the diffusion process from user to object, and from object to user to be equivalent. We show in this work that it is not the case and we improve the quality of the recommendation by taking into account the asymmetrical nature of this process. We apply this idea to modify the state-of-the-art recommendation methods. The simulation results show that the new methods can outperform these existing methods in both recommendation accuracy and diversity. Finally, this modification is checked to be able to improve the recommendation in a realistic case.     

Visual and buying sequence features-based product image recommendation using optimization based deep residual network

A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.     

Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems

In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ²) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.     

Calculation of protein ionization equilibria with conformational sampling: pK(a) of a model leucine zipper, GCN4 and barnase.

The use of conformational ensembles provided by nuclear magnetic resonance (NMR) experiments or generated by molecular dynamics (MD) simulations has been regarded as a useful approach to account for protein motions in the context of pK(a) calculations, yet the idea has been tested occasionally. This is the first report of systematic comparison of pK(a) estimates computed from long multiple MD simulations and NMR ensembles. As model systems, a synthetic leucine zipper, the naturally occurring coiled coil GCN4, and barnase were used. A variety of conformational averaging and titration curve-averaging techniques, or combination thereof, was adopted and/or modified to investigate the effect of extensive global conformational sampling on the accuracy of pK(a) calculations. Clustering of coordinates is proposed as an approach to reduce the vast diversity of MD ensembles to a few structures representative of the average electrostatic properties of the system in solution. Remarkable improvement of the accuracy of pK(a) predictions was achieved by the use of multiple MD simulations. By using multiple trajectories the absolute error in pK(a) predictions for the model leucine zipper was reduced to as low as approximately 0.25 pK(a) units. The validity, advantages, and limitations of explicit conformational sampling by MD, compared with the use of an average structure and a high internal protein dielectric value as means to improve the accuracy of pK(a) calculations, are discussed.     

CogTime_RMF: regularized matrix factorization with drifting cognition degree for collaborative filtering

Due to the exponential growth of information, recommender systems have been a widely exploited technique to solve the problem of information overload effectively. Collaborative filtering (CF) is the most successful and extensively employed recommendation approach. However, current CF methods recommend suitable items for users mainly by user-item matrix that contains the individual preference of users for items in a collection. So these methods suffer from such problems as the sparsity of the available data and low accuracy in predictions. To address these issues, borrowing the idea of cognition degree from cognitive psychology and employing the regularized matrix factorization (RMF) as the basic model, we propose a novel drifting cognition degree-based RMF collaborative filtering method named CogTime_RMF that incorporates both user-item matrix and users’ drifting cognition degree with time. Moreover, we conduct experiments on the real datasets MovieLens 1 M and MovieLens 100 k, and the method is compared with three similarity based methods and three other latest matrix factorization based methods. Empirical results demonstrate that our proposal can yield better performance over other methods in accuracy of recommendation. In addition, results show that CogTime_RMF can alleviate the data sparsity, particularly in the circumstance that few ratings are observed.     

Altered small-world brain networks in schizophrenia patients during working memory performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.     

Empirical Study of User Preferences Based on Rating Data of Movies

User preference plays a prominent role in many fields, including electronic commerce, social opinion, and Internet search engines. Particularly in recommender systems, it directly influences the accuracy of the recommendation. Though many methods have been presented, most of these have only focused on how to improve the recommendation results. In this paper, we introduce an empirical study of user preferences based on a set of rating data about movies. We develop a simple statistical method to investigate the characteristics of user preferences. We find that the movies have potential characteristics of closure, which results in the formation of numerous cliques with a power-law size distribution. We also find that a user related to a small clique always has similar opinions on the movies in this clique. Then, we suggest a user preference model, which can eliminate the predictions that are considered to be impracticable. Numerical results show that the model can reflect user preference with remarkable accuracy when data elimination is allowed, and random factors in the rating data make prediction error inevitable. In further research, we will investigate many other rating data sets to examine the universality of our findings.     

Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.     

Properties of hog cholera viruses recently isolated in Japan

Hog cholera (HC) viruses newly isolated in Japan in 1980 and 1981 were examined for pathogenicity and serological properties by the neutralization test with antisera against bovine viral diarrhea-mucosal disease (BVD . MD) and HC viruses. Five of 23 isolates examined were neutralized poorly by BVD . MD antibody, but well by HC antibody. On the contrary, 15 isolates were neutralized readily and two isolates moderately by BVD . MD antibody. The other one reacted poorly with either HC or BVD . MD antibodies. The isolate neutralized poorly by BVD . MD antibody was more highly pathogenic than those neutralized readily. It was concluded that the antigenic properties and pathogenicity of the HC viruses were not monotype , and that HC viruses varying in antigenicity and pathogenicity were present in the field.     

Low cost multi user multi platform accessible HPLC data acquisition system

Summary A low cost multi user multi platform accessible HPLC data acquisition system has been designed for use in a laboratory environment. This system uses available HPLC measurement systems that lack modern network communication tools and a low cost computer with reliable software. HPLC data are portable to any other computer by means of File Transport Protocol (FTP) communication and can then be used for data analysis. Off line analysis of ethanol data showed a substantial improvement over the old system in terms of data accuracy and skewness. Furthermore, off line data analysis could resolve hidden acetaldehyde peaks which revealed to be oscillatory.     

Measuring User Similarity Using Electric Circuit Analysis: Application to Collaborative Filtering

We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user–item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems     

Symmetry‐restrained molecular dynamics simulations improve homology models of potassium channels

Most crystallized homo‐oligomeric ion channels are highly symmetric, which dramatically decreases conformational space and facilitates building homology models (HMs). However, in molecular dynamics (MD) simulations channels deviate from ideal symmetry and accumulate thermal defects, which complicate the refinement of HMs using MD. In this work we evaluate the ability of symmetry constrained MD simulations to improve HMs accuracy, using an approach conceptually similar to Critical Assessment of techniques for protein Structure Prediction (CASP) competition: build HMs of channels with known structure and evaluate the efficiency of proposed methods in improving HMs accuracy (measured as deviation from experimental structure). Results indicate that unrestrained MD does not improve the accuracy of HMs, instantaneous symmetrization improves accuracy but not stability of HMs during subsequent unrestrained MD, while gradually imposing symmetry constraints improves both accuracy (by 5–50%) and stability of HMs. Moreover, accuracy and stability are strongly correlated, making stability a reliable criterion in predicting the accuracy of new HMs. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Comparison of genetic variation between the seed bank and above ground vegetation of a wetland species

The seed bank is thought to be important for the conservation and restoration of the genetic diversity of the above ground plant population. It has been proposed that it may be no use to compare the genetic diversity between seed bank and above ground plants if there was no habitat fragmentation. Whether this prediction holds for aquatic plants is still unknown. Here, ISSR analysis of a common wetland species Heleocharis valleculosa (Cyperaceae) was conducted to evaluate if the seed bank has higher, equal or lower genetic variation than the above ground vegetation. Our results showed high genetic diversity in the seed bank, so the seedbank can act as a storehouse of genetic variation for the above ground vegetation, especially in unfavorable conditions. Thus, unlike terrestrial habitats where genetic diversity is often low, wetlands that are connected with water flow can accumulate many genotypes in the seed bank and the question of how genetic diversity varies above and below ground is still open.     

Effects of Proton Pump Inhibitors on the Gastrointestinal Microbiota in Gastroesophageal Reflux Disease

Proton pump inhibitors(PPIs) are commonly used to lessen symptoms in patients with gastroesophageal reflux disease(GERD). However, the effects of PPI therapy on the gastrointestinal microbiota in GERD patients remain unclear. We examined the association between the PPI usage and the microbiota present in gastric mucosal and fecal samples from GERD patients and healthy controls(HCs) using 16 S rRNA gene sequencing. GERD patients taking PPIs were further divided into short-term and long-term PPI user groups. We showed that PPI administration lowered the relative bacterial diversity of the gastric microbiota in GERD patients. Compared to the non-PPIuser and HC groups, higher abundances of Planococcaceae, Oxalobacteraceae, and Sphingomonadaceae were found in the gastric microbiota from the PPI-user group. In addition, the Methylophilus genus was more highly abundant in the long-term PPI user group than in the short-term PPI-user group. Despite the absence of differences in alpha diversity, there were significant differences in the fecal bacterial composition of between GERD patients taking PPIs and those not taking PPIs. There was a higher abundance of Streptococcaceae, Veillonellaceae, Acidaminococcaceae,Micrococcaceae, and Flavobacteriaceae present in the fecal microbiota from the PPI-user group than those from the non-PPI-user and HC groups. Additionally, a significantly higher abundance of Ruminococcus was found in GERD patients on long-term PPI medication than that on shortterm PPI medication. Our study indicates that PPI administration in patients with GERD has a significant effect on the abundance and structure of the gastric mucosal microbiota but only on the composition of the fecal microbiota.     

Above‐ and Belowground Trophic Interactions on Creeping Thistle (Cirsium arvense) in High‐ and Low‐Diversity Plant Communities: Potential for Biotic Resistance?

The capacity of local communities to control introduced plants is called biotic resistance. Biotic resistance has been almost exclusively tested for plant competition and above ground herbivores and pathogens, while neglecting root herbivores and soil pathogens. Here, we present biotic resistance by above- and below ground herbivores in concert, and relate the abundance of the plant enemies to the species diversity of the local plant communities. The study was carried out in a 7-year-old biodiversity field experiment. We used creeping thistle (Cirsium arvense) as a model, and quantified sap-sucking herbivores: above ground aphids, their antagonists, and root-feeding nematodes. As plant diversity treatments, we used field plots sown with high (15 plant species, HSD) or low (4 plant species, LSD) diverse seed mixtures in 1996 and that were not weeded. Creeping thistle became established spontaneously at the start of the experiment. In 2002, in HSD plots, 90 % of the plant community was made up by 11 species, compared to seven species in LSD plots. No differences were found for C. arvense abundance or biomass. Above ground, three aphid species were found on C. arvense-Uroleucon cirsii, Aphis fabae, and Macrosiphum euphorbiae, but the latter was found only in low densities. Significantly more aphid species were found on individual plants in HSD plots. Moreover, in HSD plots, on average 10 % of aphids were parasitized, while no parasitism was observed in LSD plots. In the root zone of C. arvense, significantly more nematodes were found in HSD than in LSD plots, and a significantly higher proportion of those nematodes were plant parasites. The dominant plant parasitic nematode in both treatments was Paratylenchus. We conclude that biotic resistance by natural enemies may be enhanced by plant species diversity, but that above- and below ground sap-sucking herbivores do not necessarily have to respond similarly to the diversity of the surrounding plant community.     

Microbial diversity and activity through a permafrost/ground ice core profile from the Canadian high Arctic

Culture‐dependent and culture‐independent methods were used in an investigation of the microbial diversity in a permafrost/massive ground ice core from the Canadian high Arctic. Denaturing gradient gel electrophoresis as well as Bacteria and Archaea 16S rRNA gene clone libraries showed differences in the composition of the microbial communities in the distinct core horizons. Microbial diversity was similar in the active layer (surface) soil, permafrost table and permafrost horizons while the ground ice microbial community showed low diversity. Bacteria and Archaea sequences related to the Actinobacteria (54%) and Crenarchaeota (100%) respectively were predominant in the active layer while the majority of sequences in the permafrost were related to the Proteobacteria (57%) and Euryarchaeota (76%). The most abundant phyla in the ground ice clone libraries were the Firmicutes (59%) and Crenarchaeota (82%). Isolates from the permafrost were both less abundant and diverse than in the active layer soil, while no culturable cells were recovered from the ground ice. Mineralization of [1‐¹⁴C] acetic acid and [2‐¹⁴C] glucose was used to detect microbial activity in the different horizons in the core. Mineralization was detected at near ambient permafrost temperatures (?15°C), indicating that permafrost may harbour an active microbial population, while the low microbial diversity, abundance and activity in ground ice suggests a less hospitable microbial habitat.     

Landscape-scale crop diversity interacts with local management to determine ground beetle diversity

Crop diversification and maintenance of semi-natural habitats (grasslands and field boundaries) are suggested to enhance farmland biodiversity, but the relative importance of these factors remains poorly known. We evaluated how crop diversity and availability of semi-natural grasslands at a landscape-scale interacted with local farming management (three management types from low to high intensity: ley < winter wheat < sugar beet) in their effect on ground beetle assemblages in southern Sweden. Ground beetle diversity increased with crop diversity either independently of local management (Simpson species diversity), or only in the less intensively managed habitats (rarefied species richness). While ground beetle diversity in leys tended to increase with field boundary length, no such relationship was observed in winter wheat or sugar beet fields. In contrast, the landscape proportions of leys and semi-natural grasslands did not affect ground beetle species richness and diversity. We conclude that (a) semi-natural grasslands and leys may not function as source habitats at a landscape-scale if they comprise a low proportion of the total land-use, while (b) increasing crop diversity is correlated to ground beetle richness and diversity in agricultural landscapes dominated by arable land. The beneficial effect of landscape-scale crop diversification on farmland biodiversity may depend on the general level of agricultural intensity of a region.     

铜鱼线粒体控制区的序列变异和遗传多样性

采用PCR和DNA测序技术研究长江中上游野生铜鱼的遗传多样性和群体遗传学特征,从9个采样点共获得100尾铜鱼,用于分析的线粒体DNA控制区的片段序列为946bp。在100个序列中,共检测出变异位点47个(其中增添/缺失位点8个),单倍型41种。9个地理群体的平均单倍型多样性(Hd)和平均核苷酸多样性(Pi)分别为0.9257±0.0162和0.004178±0.002337,表现出较贫乏的遗传多样性。群体间的分化指数(FST值)、平均基因流(Nm)、分子方差分析(AMOVA)和平均K2-P遗传距离均表明9个铜鱼地理群体间存在广泛的基因交流,未明显发生群体遗传分化。另外,共享单倍型比例较高,约为34%(14/41)。单倍型的UPGMA分子系统树和简约网络图显示单倍型的聚类与地理分群没有相关性。上述结果表明9个铜鱼地理群体属于同一种群。       

Comparison of force fields to study the zinc-finger containing protein NPL4, a target for disulfiram in cancer therapy

Molecular dynamics (MD) simulations are a powerful approach to studying the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging. NPL4 is a zinc-binding protein and works as a cofactor for p97 to regulate protein homeostasis. NPL4 is of biomedical importance and has been proposed as the target of disulfiram, a drug recently repurposed for cancer treatment. Experimental studies proposed that the disulfiram metabolites, bis-(diethyldithiocarbamate)‑copper and cupric ions, induce NPL4 misfolding and aggregation. However, the molecular details of their interactions with NPL4 and consequent structural effects are still elusive. Here, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We examined different sets of non-bonded parameters because we want to study the misfolding mechanism and cannot rule out that the zinc may detach from the protein during the process and copper replaces it. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of NPL4. Furthermore, we investigated the performance of a force field including bonded parameters to treat copper ions in NPL4 that we obtained based on QM calculations.