Miracle-dz/UniFAR-FTUs huggingface dataset:
The optimal mean variance problem with inflation

The risk of inflation is looming under the current low interest rate environment. Assuming that the investment includes a fixed interest asset and $n$ risky assets under inflation, we consider two scenarios: inflation rate can be observed directly or through a noisy observation. Since the inflation rate is random, all assets become risky. Under this circumstance, we formulate the portfolio selection problem and derive the efficient frontier by solving the associated HJB equation. We find that for a given expected portfolio return, investment at time $t$ is linearly proportional to the price index level. Moreover, the risk for the real value of the portfolio is no longer minimal when all the wealth is put into the fixed interest asset. Finally, for the mutual fund theorem, two funds are needed now instead of the traditional single fund. If an inflation linked bond can be included in the portfolio, the problem is reduced to the traditional mean variance problem with a risk-free and $n+1$ risky assets with real returns.
Environmentally realistic concentrations of chlorinated, brominated, and fluorinated persistent organic pollutants induce the unfolded protein response as a shared stress pathway in the liver of Atlantic cod (Gadus morhua)

In the North Sea and North Atlantic coastal areas, fish experience relatively high background levels of persistent organic pollutants. This study aimed to compare the mode of action of environmentally relevant concentrations of mixtures of halogenated compounds in Atlantic cod. Juvenile male cod with mean weight of 840 g were exposed by gavage to dietary mixtures of chlorinated (PCBs, DDT analogs, chlordane, lindane, and toxaphene), brominated (PBDEs), and fluorinated (PFOS) compounds for 4 weeks. One group received a combined mixture of all three compound groups. The results showed that the accumulated levels of chemicals in cod liver after 4 weeks of exposure reflected concentrations found in wild fish in this region. Pathway analysis revealed that the treatment effects by each of the three groups of chemicals (chlorinated, brominated, and fluorinated) converged on activation of the unfolded protein response (UPR). Upstream regulator analysis predicted that almost all the key transcription factors (XBP1, ERN1, ATF4, EIF2AK3, and NFE2L2) regulating the UPR were significantly activated. No additive effect was observed in cod co‐treated with all three compound groups. In conclusion, the genome‐wide transcriptomic study suggests that the UPR pathway is a sensitive common target of halogenated organic environmental pollutants in fish.
Membrane vesicle engineering with “à la carte” bacterial‐immunogenic molecules for organism‐free plant vaccination

The United Nations heralds a world population exponential increase exceeding 9.7 billion by 2050. This poses the challenge of covering the nutritional needs of an overpopulated world by the hand of preserving the environment. Extensive agriculture practices harnessed the employment of fertilizers and pesticides to boost crop productivity and prevent economic and harvest yield losses attributed to plagues and diseases. Unfortunately, the concomitant hazardous effects stemmed from such agriculture techniques are cumbersome, that is, biodiversity loss, soils and waters contaminations, and human and animal poisoning. Hence, the so‐called ‘green agriculture’ research revolves around designing novel biopesticides and plant growth‐promoting bio‐agents to the end of curbing the detrimental effects. In this field, microbe–plant interactions studies offer multiple possibilities for reshaping the plant holobiont physiology to its benefit. Along these lines, bacterial extracellular membrane vesicles emerge as an appealing molecular tool to capitalize on. These nanoparticles convey a manifold of molecules that mediate intricate bacteria–plant interactions including plant immunomodulation. Herein, we bring into the spotlight bacterial extracellular membrane vesicle engineering to encase immunomodulatory effectors into their cargo for their application as biocontrol agents. The overarching goal is achieving plant priming by deploying its innate immune responses thereby preventing upcoming infections.
Improved Symmetric Gauss-Seidel Method for Solving Sparse Linear Systems Appear in CFD

Two new variants of iterative solvers for sparse linear systems of algebraic equations that appear in Computational Fluid Dynamics (CFD) are proposed. The proposed methods are compared with selected Krylov space methods in terms of CPU time required to achieve prescribed accuracy for selected test problems. When the coefficient matrix is diagonally dominant there are no significant differences between efficiency of methods and all of them are quite applicable. For the most demanding problems corresponding to the pressure equation in a incompressible fluid flow solver, the new proposed methods show a great advantage over the popular GMRES(5) method and they are comparable with PTFQMR method with the simple ILU preconditioner (corresponding the Symmetric Gauss-Seidel method).
New methods for deblending spectral energy distributions in confused imaging

The submillimetre band is ideal for studying high-redshift star-forming galaxies, but such studies are hampered by the poor resolution of single-dish telescopes. Interferometric follow-up has shown that many sources are in fact comprised of multiple sources. For many such targets, confusion-limited Herschel observations that target the peak of their far-infrared emission are also available. Many methods for analysing these data have been developed, but most follow the traditional approach of extracting fluxes before model spectral energy distributions are fit, which erases degeneracies among fitting parameters and glosses over the intricacies of confusion noise. We have developed a forward-modelling method in order to tackle this problem in a more statistically rigorous way, which combines source deblending and spectral energy distribution fitting into the same procedure. We adapt our method to three independent projects, all of which benefit from our improved methodology. We investigate a “giant submillimetre arc” behind a massive foreground cluster and uncover seven multiply imaged galaxies, of which six are found to be at a redshift of z ∼ 2.9, and possibly constitute an interacting galaxy group. Using our new method, we disentangle the arc into its contributing components and constrain their far-IR properties. Using confusion limited Herschel-SPIRE imaging, the far-IR properties LABOCA detected submillimetre sources can be constrained. Despite such sources often breaking up in highresolution ALMA imaging, existing studies have implemented traditional fitting methods. We apply our new forward modelling method to re-derive constraints on the far-infrared properties of these sources, exploring selection effects on this sample, while highlighting the benefits of our fitting approach. Finally, we present SCUBA-2 follow-up of 51 candidate proto-cluster fields undergoing enhanced star-formation. With the accompanying Herschel-SPIRE observations and a realistic dust temperature prior, we provide photometric redshift and far-IR luminosity estimates for 172 SCUBA-2 selected sources within the Planck overdensity fields. We find a redshift distribution similar to sources found in cosmological surveys, although our fields are enhanced in both density of sources and star formation rate density over a wide range of redshifts.
A framework based on the information domain to measure coupling changes in electrophysiological signals

Objectives. In this paper, the features of physiological signals of healthy dataset are extracted using the linear and non-linear techniques, and a comparison has been made on healthy young and old subjects to study the aging and gender-related changes in the contribution of Heart Rate (HR), Blood Pressure (BP), and Respiration (RESP). Methods. To quantify the coupling changes in cardiovascular, cardiorespiratory, and vasculorespiratory complexity, an information domain approach based on compensated transfer entropy (cTE) is proposed. Result. The results show that there is a substantial decrease in the flow of information from BP tro the time interval between successive R-peaks (RR) and from RR to BP. There is also a significant decrease in the flow of information from RESP to BP and RESP to RR but there is no significant change in the information flow from BP to RESP and RR to RESP. Conclusion. We have done linear and non-linear analysis on the healthy datasets of young and old subjects. As already existed techniques lacks in studying complex behaviours of electrophysiological signals so to overcome these limitations, we have proposed compensated transfer entropy (cTE). We conducted an investigation to determine the degree to which recordings of RESP, BP, and HR can be utilized to predict changes in the other parameters. Specifically, the proposed analysis examined the relationship between these variables and assessed their consistency across different age groups and genders. By analyzing the data, we aimed to gain insights into the interdependencies and predictive potential of these physiological measures in relation to each other.
Reconstruction of Gaps in Flow Series Using Singular Spectrum Analysis (SSA) and Multi-channel SSA (M-SSA)

SSA: Singular Spectrum Analysis (SSA) (Broomhead and King, 1986a; Fraedrich, 1986; Ghill et al., 2002) is a state-of-the-art spectral, dataadaptive and nonparametric method. SSA decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The term singular spectrum comes from the spectral (eigenvalue) decomposition of a matrix into its spectrum of eigenvalues (Elsner and Tsonis, 1996). The basic SSA algorithm has stages of decomposition and reconstruction. The decomposition stage requires embedding and singular value decomposition. In this stage, there are two basic steps. The former is embedding the sampled time series in a vector space of dimension M, the latter is computing the MxM lag-covariance matrix of the data (Vautard and Ghill, 1989). The reconstruction stage demands the grouping to make subgroups of the decomposed trajectory matrices and diagonal averaging to reconstruct the new time series from the subgroups (Myung, 2009).
Biologics to treat anaphylaxis

Purpose of review The purpose of this literature review was to review the latest use of biologics in the management of anaphylaxis. The methodology was to highlight both the nonbiologic management of anaphylaxis and the biologic management and how they can be used in conjunction with each other. Recent findings As the phenotypes and endotypes of anaphylaxis are better portrayed, it furthers our understanding of the mechanisms of anaphylaxis. New applications of existing biologics to the prevention of anaphylaxis are described. Summary Anaphylaxis is a potentially life-threatening acute hypersensitivity reaction affecting up to 16.8% of the U.S. population. Acute management entails swift identification, removal of the causative agent, and the prevention of cardiovascular collapse, firstly with epinephrine. Adjunctive treatments such as antihistamines work to prevent anaphylaxis from recurring. Biologic management of anaphylaxis involves the use of large-molecule drugs such as monoclonal antibodies. Omalizumab, an IgG1 monoclonal antibody targeting unbound IgE, is the most prevalent and widely studied biologic in the prevention of anaphylaxis. Other monoclonal antibodies in development or approved for other indications, such as ligelizumab, quilizumab, MEDI4212, and dupilumab, may also have potential for preventing anaphylaxis through various mechanisms.
A Review on Dynamic Clustering: Using Density Metrics

: Clustering of high dimensional dynamic data is challenging problem. Within the frame of big data analysis, the computational effort needed to perform the clustering task may become prohibitive and motivated the construction of several algorithms or the adaptation of existing ones, as the well known K-means algorithm. One of the critical problem in k-means, k-menoid, k-means or other clustering algorithms required to pre-assigned no. of k which cannot detect non-spherical clusters. With the existing RLClu algorithm needs users to pre-assign two minimum thresholds of the local density and the minimum density-based distance. Clustering is the process of data classification when none prior knowledge required for classification. To overcome these problems STClu clustering algorithm is proposed. In this algorithm a new metric is defined to evaluate the local density of each object, which shows better performance in distinguishing different objects. Furthermore, an outward statistical test method is used to identify the clustering centers automatically on a centrality metric constructed based on the new local density and new minimum density-based distance. Dynamic clustering is an approach to get and extract clusters in real time environments. It has much application such as, data warehousing, sensor network etc. Therefore there is need of such technique in which the data set is increasing in size over time by adding more and more data.
Genome-Scale Methylation Analysis Identifies Immune Profiles and Age Acceleration Associations with Bladder Cancer Outcomes

Abstract Background: Immune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival. Methods: Bladder cancer case peripheral blood DNA methylation was measured using the Illumina HumanMethylationEPIC array. Extended cell-type deconvolution quantified 12 immune cell-type proportions, including memory, naïve T and B cells, and granulocyte subtypes. DNA methylation clocks determined biological age. Cox proportional hazards models tested associations of immune cell profiles and age acceleration with bladder cancer outcomes. The partDSA algorithm discriminated 10-year overall survival groups from clinical variables and immune cell profiles, and a semi-supervised recursively partitioned mixture model (SS-RPMM) with DNA methylation data was applied to identify a classifier for 10-year overall survival. Results: Higher CD8T memory cell proportions were associated with better overall survival [HR = 0.95, 95% confidence interval (CI) = 0.93–0.98], while higher neutrophil-to-lymphocyte ratio (HR = 1.36, 95% CI = 1.23–1.50), CD8T naïve (HR = 1.21, 95% CI = 1.04–1.41), neutrophil (HR = 1.04, 95% CI = 1.03–1.06) proportions, and age acceleration (HR = 1.06, 95% CI = 1.03–1.08) were associated with worse overall survival in patient with bladder cancer. partDSA and SS-RPMM classified five groups of subjects with significant differences in overall survival. Conclusions: We identified associations between immune cell subtypes and age acceleration with bladder cancer outcomes. Impact: The findings of this study suggest that bladder cancer outcomes are associated with specific methylation-derived immune cell-type proportions and age acceleration, and these factors could be potential prognostic biomarkers.
Improving accuracy of Winograd convolution for DNNs

Modern deep neural networks (DNNs) spend a large amount of their execution time computing convolutions. Winograd's minimal algorithm for small convolutions can greatly reduce the number of arithmetic operations. However, a large reduction in floating point (FP) operations in these algorithms can result in significantly reduced FP accuracy of the result. In this paper we propose several methods for reducing the FP error of these algorithms. Minimal convolution algorithms depend on the selection of several numeric \textit{points} that have a large impact on the accuracy of the result. Some points are known to be better than others, but there is no systematic method selecting points for small convolutions. We show that there are a relatively small number of important cases for DNN convolution, that can be searched empirically. We compared both standard and modified versions of the Winograd algorithm. Further, we demonstrate that both the ordering and value of the points is important, and we propose a canonical evaluation ordering that both reduces FP error and the size of the search space based on Huffman coding. We find that good point selections depend on the values of the points themselves and on symmetries between different points. We show that sets of points with symmetric groups give better results. In addition, we explore other methods to reduce FP error, including mixed-precision convolution, and pairwise addition across DNN channels. Using our methods we can significantly reduce FP error for a given Winograd convolution block size, which allows larger block sizes and reduced computation.
Scattering transforms and classification of hyperspectral images

We explore the representation capabilities of scattering transforms for the classification of hyperspectral images. We examine several types, including a recently developed technique called the Fourier scattering transform. This method is naturally suited for the representation of hyperspectral data because it decomposes signals into multi-frequency bands and removes small perturbations such as noise. We test on four standard hyperspectral datasets, and the results indicate that the Fourier scattering transform is effective at representing spectral data. We also present a spatial-spectral scattering transform that combines Wavelet and Fourier representations, and this method obtains significantly higher classification accuracies.
Comparative Analysis of miRNA Expression after Whole-Body Irradiation Across Three Strains of Mice

Whole- or partial-body exposure to ionizing radiation damages major organ systems, leading to dysfunction on both acute and chronic timescales. Radiation medical countermeasures can mitigate acute damages and may delay chronic effects when delivered within days after exposure. However, in the event of widespread radiation exposure, there will inevitably be scarce resources with limited countermeasures to distribute among the affected population. Radiation biodosimetry is necessary to separate exposed from unexposed victims and determine those who requires the most urgent care. Blood-based, microRNA signatures have great potential for biodosimetry, but the affected population in such a situation will be genetically heterogeneous and have varying miRNA responses to radiation. Thus, there is a need to understand differences in radiation-induced miRNA expression across different genetic backgrounds to develop a robust signature. We used inbred mouse strains C3H/HeJ and BALB/c mice to determine how accurate miRNA in blood would be in developing markers for radiation vs. no radiation, low dose (1 Gy, 2 Gy) vs. high dose (4 Gy, 8 Gy), and high risk (8 Gy) vs. low risk (1 Gy, 2 Gy, 4 Gy). Mice were exposed to whole-body doses of 0 Gy, 1 Gy, 2 Gy, 4 Gy, or 8 Gy of X rays. MiRNA expression changes were identified using NanoString nCounter panels on blood RNA collected 1, 2, 3 or 7 days postirradiation. Overall, C3H/HeJ mice had more differentially expressed miRNAs across all doses and timepoints than BALB/c mice. The highest amount of differential expression occurred at days 2 and 3 postirradiation for both strains. Comparison of C3H/HeJ and BALB/c expression profiles to those previously identified in C57BL/6 mice revealed 12 miRNAs that were commonly expressed across all three strains, only one of which, miR-340-5p, displayed a consistent regulation pattern in all three miRNA data. Notably multiple Let-7 family members predicted high-dose and high-risk radiation exposure (Let-7a, Let-7f, Let-7e, Let-7g, and Let-7d). KEGG pathway analysis demonstrated involvement of these predicted miRNAs in pathways related to: Fatty acid metabolism, Lysine degradation and FoxO signaling. These findings indicate differences in the miRNA response to radiation across various genetic backgrounds, and highlights key similarities, which we exploited to discover miRNAs that predict radiation exposure. Our study demonstrates the need and the utility of including multiple animal strains in developing and validating biodosimetry diagnostic signatures. From this data, we developed highly accurate miRNA signatures capable of predicting exposed and unexposed subjects within a genetically heterogeneous population as quickly as 24 h of exposure to radiation.
Intravenous ibuprofen in postoperative pain and fever management in adults: A systematic review and meta‐analysis of randomized controlled trials

This study aims to evaluate the efficacy and safety of multiple or single‐dosage intravenous ibuprofen (IVIB) in managing postoperative pain and fever in adults who are unable to take oral medications. A systematic review and meta‐analysis was conducted based on randomized controlled trials (RCTs) comparing IVIB with placebo or other analgesic and antipyretic medications for postoperative pain and fever management. Data were collected from 8 main databases from the inception to June 2022. Risk of bias assessment was performed, and the GRADE methodology was used to assess the certainty of pooled evidence. Primary outcomes included visual analogue scale (VAS) scores within 24 h postoperative and reduction of temperature. Meta‐analyses were conducted to calculate the mean difference (MD) or risk ratios (RR) and 95% CIs. As a result, a total of twenty‐three RCTs with 3716 participants were included. For postoperative pain, with moderate‐to‐low certainty evidence, IVIB was associated with lower postoperative VAS scores than placebo, with MD ranging from −3.53 (95% CI, −4.32 to −2.75) at 0 min to −0.96 (95% CI, −1.35 to −0.57) at 24 h. Compared with intravenous acetaminophen, IVIB demonstrated lower VAS scores (MD, −1.54 at 0 min; −0.36 at 24 h). For fever, IVIB showed satisfactory antipyretic efficiency in a short period of time, but no difference was observed between IVIB and intravenous acetaminophen. IVIB was well‐tolerated for both pain and fever management. In conclusion, moderate‐to‐low certainty evidence supports the use of IVIB for adults with postoperative pain and fever who are unable to take oral medications.
Haptic Exploration of Unknown Objects for Robust In-Hand Manipulation

Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects.