ZhiWo: Activity tagging and recognizing system from personal lifelogs

With the increasing use of mobile devices as personal record- ing, communication and sensing tools, extracting the seman- tics of life activities through sensed data (photos, accelerom- eter, GPS etc.) is gaining widespread public awareness. A person who engages in long-term personal sensing is engag- ing in a process of lifelogging. Lifelogging typically involves using a range of (wearable) sensors to capture raw data, to segment into discrete activities, to annotate and subse- quently to make accessible by search or browsing tools. In this paper, we present an intuitive lifelog activity record- ing and management system called ZhiWo. By using a su- pervised machine learning approach, sensed data collected by mobile devices are automatically classified into different types of daily human activities and these activities are inter- preted as life activity retrieval units for personal archives

From lifelog to diary: a timeline view for memory reminiscence

As digital recording sensors and lifelogging devices become more prevalent, the suitability of lifelogging tools to act as a reminiscence supporting tool has become an important research challenge. This paper aims to describe a rst- generation memory reminiscence tool that utilises lifelog- ging sensors to record a digital diary of user activities and presents it as a narrative description of user activities. The automatically recognised daily activities are shown chronologically in the timeline view

Proceedings of RIKEN BNL Research Center Workshop: Brookhaven Summer Program on Quarkonium Production in Elementary and Heavy Ion Collisions

Understanding the structure of the hadron is of fundamental importance in subatomic physics. Production of heavy quarkonia is arguably one of the most fascinating subjects in strong interaction physics. It offers unique perspectives into the formation of QCD bound states. Heavy quarkonia are among the most studied particles both theoretically and experimentally. They have been, and continue to be, the focus of measurements in all high energy colliders around the world. Because of their distinct multiple mass scales, heavy quarkonia were suggested as a probe of the hot quark-gluon matter produced in heavy-ion collisions; and their production has been one of the main subjects of the experimental heavy-ion programs at the SPS and RHIC. However, since the discovery of J/psi at Brookhaven National Laboratory and SLAC National Accelerator Laboratory over 36 years ago, theorists still have not been able to fully understand the production mechanism of heavy quarkonia, although major progresses have been made in recent years. With this in mind, a two-week program on quarkonium production was organized at BNL on June 6-17, 2011. Many new experimental data from LHC and from RHIC were presented during the program, including results from the LHC heavy ion run. To analyze and correctly interpret these measurements, and in order to quantify properties of the hot matter produced in heavy-ion collisions, it is necessary to improve our theoretical understanding of quarkonium production. Therefore, a wide range of theoretical aspects on the production mechanism in the vacuum as well as in cold nuclear and hot quark-gluon medium were discussed during the program from the controlled calculations in QCD and its effective theories such as NRQCD to various models, and to the first principle lattice calculation. The scientific program was divided into three major scientific parts: basic production mechanism for heavy quarkonium in vacuum or in high energy elementary collisions; the formation of quarkonium in nuclear medium as well as the strong interacting quark-gluon matter produced in heavy ion collisions; and heavy quarkonium properties from the first principle lattice calculations. The heavy quarkonium production at a future Electron-Ion Collider (EIC) was also discussed at the meeting. The highlight of the meeting was the apparent success of the NRQCD approach at next-to-leading order in the description of the quarkonium production in proton-proton, electron-proton and electron positron collisions. Still many questions remain open in lattice calculations of in-medium quarkonium properties and in the area of cold nuclear matter effects

A comprehensive analysis of the Cupin gene family in soybean (Glycine max)

Publisher's Version/PDFCupin superfamily of proteins, including germin and germin-like proteins (GLPs) from higher plants, is known to play crucial roles in plant development and defense. To date, no systematic analysis has been conducted in soybean (Glycine max) incorporating genome organization, gene structure, expression compendium. In this study, 69 putative Cupin genes were identified from the whole-genome of soybean, which were non-randomly distributed on 17 of the 20 chromosomes. These Gmcupin proteins were phylogenetically clustered into ten distinct subgroups among which the gene structures were highly conserved. Eighteen pairs (52.2%) of duplicate paralogous genes were preferentially retained in duplicated regions of the soybean genome. The distributions of GmCupin genes implied that long segmental duplications contributed significantly to the expansion of the GmCupin gene family. According to the RNA-seq data analysis, most of the Gmcupins were differentially expressed in tissue-specific expression pattern and the expression of some duplicate genes were partially redundant while others showed functional diversity, suggesting the Gmcupins have been retained by substantial subfunctionalization during soybean evolutionary processes. Selective analysis based on single nucleotide polymorphisms (SNPs) in cultivated and wild soybeans revealed sixteen Gmcupins had selected site(s), with all SNPs in Gmcupin10.3 and Gmcupin07.2 genes were selected sites, which implied these genes may have undergone strong selection effects during soybean domestication. Taken together, our results contribute to the functional characterization of Gmcupin genes in soybean

MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos

Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless, numerous limitations exist within existing public MSMO datasets, including insufficient maintenance, data inaccessibility, limited size, and the absence of proper categorization, which pose significant challenges. To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset. Our new dataset features (1) Human-validated summaries for both video and textual content, providing superior human instruction and labels for multimodal learning. (2) Comprehensively and meticulously arranged categorization, spanning 17 principal categories and 170 subcategories to encapsulate a diverse array of real-world scenarios. (3) Benchmark tests performed on the proposed dataset to assess various tasks and methods, including \textit{video summarization}, \textit{text summarization}, and \textit{multimodal summarization}. To champion accessibility and collaboration, we will release the \textbf{MMSum} dataset and the data collection tool as fully open-source resources, fostering transparency and accelerating future developments. Our project website can be found at~\url{https://mmsum-dataset.github.io/}Comment: Project website: https://mmsum-dataset.github.io

Identification of the Genomic Region Underlying Seed Weight per Plant in Soybean (Glycine max L. Merr.) via High-Throughput Single-Nucleotide Polymorphisms and a Genome-Wide Association Study

Seed weight per plant (SWPP) of soybean (Glycine max (L.) Merr.), a complicated quantitative trait controlled by multiple genes, was positively associated with soybean seed yields. In the present study, a natural soybean population containing 185 diverse accessions primarily from China was used to analyze the genetic basis of SWPP via genome-wide association analysis (GWAS) based on high-throughput single-nucleotide polymorphisms (SNPs) generated by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) method. A total of 33,149 SNPs were finally identified with minor allele frequencies (MAF) > 5% which were present in 97% of all the genotypes. Twenty association signals associated with SWPP were detected via GWAS. Among these signals, eight SNPs were novel loci, and the other twelve SNPs were overlapped or located in the linked genomic regions of the reported QTL from SoyBase database. Several genes belonging to the categories of hormone pathways, RNA regulation of transcription in plant development, ubiquitin, transporting systems, and other metabolisms were considered as candidate genes associated with SWPP. Furthermore, nine genes from the flanking region of Gm07:19488264, Gm08:15768591, Gm08:15768603, or Gm18:23052511 were significantly associated with SWPP and were stable among multiple environments. Nine out of 18 haplotypes from nine genes showed the effect of increasing SWPP. The identified loci along with the beneficial alleles and candidate genes could be of great value for studying the molecular mechanisms underlying SWPP and for improving the potential seed yield of soybean in the future

MODIS-based Daily Lake Ice Extent and Coverage dataset for Tibetan Plateau

ABSTRACTThe Tibetan Plateau houses numerous lakes, the phenology and duration of lake ice in this region are sensitive to regional and global climate change, and as such are used as key indicators in climate change research, particularly in environment change comparison studies for the Earth three poles. However, due to its harsh natural environment and sparse population, there is a lack of conventional in situ measurement on lake ice phenology. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) data, which can be traced back 20 years with a 500 m spatial  resolution, were used to monitor lake ice for filling the observation gaps. Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm, and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps. Through time series analysis of every single lake with size greater than 3 km2 in size, 308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset, including 216 lakes that can be further retrieved with four determinable lake ice parameters: Freeze-up Start (FUS), Freeze-up End (FUE), Break-up Start (BUS), and Break-up End (BUE), and 92 lakes with two parameters, FUS and BUE. Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing. The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13. The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations. The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.Peer reviewe

Ultrafast Solvation Dynamics of Human Serum Albumin: Correlations with Conformational Transitions and Site-Selected Recognition

Human serum albumin, the most abundant protein found in blood plasma, transports a great variety of ligands in the circulatory system and undergoes reversible conformational transitions over a wide range of pH values. We report here our systematic studies of solvation dynamics and local rigidity in these conformations using a single intrinsic tryptophan (W214) residue as a local molecular probe. With femtosecond resolution, we observed a robust bimodal distribution of time scales for all conformational isomers. The initial solvation occurs in several picoseconds, representing the local librational/rotational motions, followed by the dynamics, in the tens to hundreds of picoseconds, which result from the more bonded water in the tryptophan crevice. Under the physiological condition of neutral pH, we measured ∼100 ps for the decay of the solvation correlation function and observed a large wobbling motion at the binding site that is deeply buried in a crevice, revealing the softness of the binding pocket and the large plasticity of the native structure. At acidic pH, the albumin molecule transforms to an extended conformation with a large charge distribution at the surface, and a similar temporal behavior was observed. However, at the basic pH, the protein opens the crevice and tightens its globular structure, and we observed significantly faster dynamics, 25−45 ps. These changes in the solvation dynamics are correlated with the conformational transitions and related to their structural integrity

Plant breeding for increased sustainability: challenges, opportunities and progress

Humanity is facing enormous challenges in the years to come: sustainability of agriculture and sustainability of our supply with food, feed and renewable materials are neither granted nor free. Especially, but not only, in the Global South, a sustainable increase in agricultural productivity and a steady reduction of avoidable losses are undoubtedly key issues that need to be addressed. In order to pinpoint the most pressing challenges and strategies to achieve targets, the United Nations have formulated the Sustainable Development Goals (https://sdgs. un.org/goals). Among these, several are directly or indirectly addressing agriculture, food supply and sustainability, most notably SDG2 (zero hunger), SDG12 (responsible consumption and production), SDG13 (climate action) and SDG15 (life on land)

A Review on Recent Computational Methods for Predicting Noncoding RNAs

Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and -mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next-and third-generation sequencing platforms to improve ncRNA prediction