sTarPicker: A Method for Efficient Prediction of Bacterial sRNA Targets Based on a Two-Step Model for Hybridization

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.Financial support was provided by the University of Oklahoma Libraries’ Open Access Fund.Ye

Kosmos-2.5: A Multimodal Literate Model

We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models

Axial Higgs Mode Detected by Quantum Pathway Interference in RTe3

The observation of the Higgs boson solidified the standard model of particle physics. However, explanations of anomalies (e.g. dark matter) rely on further symmetry breaking calling for an undiscovered axial Higgs mode. In condensed matter the Higgs was seen in magnetic, superconducting and charge density wave(CDW) systems. Uncovering a low energy mode's vector properties is challenging, requiring going beyond typical spectroscopic or scattering techniques. Here, we discover an axial Higgs mode in the CDW system RTe3 using the interference of quantum pathways. In RTe3 (R=La,Gd), the electronic ordering couples bands of equal or different angular momenta. As such, the Raman scattering tensor associated to the Higgs mode contains both symmetric and antisymmetric components, which can be excited via two distinct, but degenerate pathways. This leads to constructive or destructive interference of these pathways, depending on the choice of the incident and Raman scattered light polarization. The qualitative behavior of the Raman spectra is well-captured by an appropriate tight-binding model including an axial Higgs mode. The elucidation of the antisymmetric component provides direct evidence that the Higgs mode contains an axial vector representation (i.e. a pseudo-angular momentum) and hints the CDW in RTe3 is unconventional. Thus we provide a means for measuring collective modes quantum properties without resorting to extreme experimental conditions

Characterization and Engineering Properties of Dry and Ponded Class-F Fly Ash

Characterization studies conducted on Class-F fly-ash specimens gathered from different producers in the southeastern United States confirm general trends reported for fly ash worldwide. Additional tests and detailed analyses explain the spread in specific gravity (interparticle porosity cenospheres), highlight the tendency to segregation and layering, and show marked ferromagnetism. Furthermore, data show that early diagenetic cementation—within days after wetting—hinders densification and produces a fabric that is prone to collapse. New procedures are specifically developed to diagnose and characterize early diagenesis, including (1) pH measurements as an indicator of diagenetic potential, (2) test protocols to assess early diagenesis using oedometer tests and shear-wave velocity, and (3) procedures to determine realizable unit weights as reference values for the analyses of contractive or dilative tendencies and instability. In the absence of early diagenetic cementation, dilative fly-ash behavior is expected in the upper ≈20  m under monotonic shear loading. Flow instability may follow the failure of the containment structure if the ponded ash is saturated and has experienced hindered densification

Analisis Portofolio Optimal Dengan Single Index Model Untuk Meminimumkan Risiko Bagi Investor Di Bursa Efek Indonesia (Studi Pada Saham Indeks Kompas 100 Periode Februari 2010-juli 2014)

Investments can be made in the capital market, capital market instruments which are mostly attractive for investors is stock. Stock provides a return in the form of capital gains and dividends yield, not only noticing the return, investors need to pay attention to the investments risk. Unsystematis risk can be minimized by forming the optimal portfolio using one of the methods that is single index model. Study purpose is to knowing the stocks forming the optimal portfolio, the proportion of funds allocated to each stocks, the level of expectation return and risk.The method used in this research is descriptive research method with a quantitative approach. The samples used were 46 stocks in Kompas 100 Index, which meets the criteria for sampling. The results showed that 12 stocks of forming optimal portfolio, the stocks of which are UNVR, TRAM, MNCN, BHIT, JSMR, BMTR, GJTL, KLBF, AALI, CPIN, AKRA, and ASRI. Stock with highest proportion of funds is TRAM (23,52%), stock with lowest proportion of funds is AALI (0,62%). Portfolio which are formed will give return expectations by 3,05477% and carry the risk for about 0,1228%