Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated

As the number of experiments that employ single-cell RNA-sequencing (scRNA-seq) grows it opens up the possibility of combining results across experiments or processing cells from the same experiment assayed in separate sequencing runs. The gain in the number of cells that can be compared comes at the cost of batch effects that may be present. Several methods have been proposed to combat this for scRNA-seq datasets. We compared seven widely used method used for batch correction of scRNA-seq datasets. We present a novel approach to measure the degree to which the methods alter the data in the process of batch correction, both at the fine scale comparing distances between cells as well as measuring effects observed across clusters of cells. We demonstrate that many of the published method are poorly calibrated in the sense that the process of correction creates measurable artifacts in the data. In particular, MNN, SCVI and LIGER performed poorly in our tests, often altering the data considerably. Batch correction with Combat, BBKNN and Seurat introduced artifacts that could be detected in our setup. However, we found that Harmony was the only method that consistently performed well, in all the testing methodology we present. Due to these result Harmony is the only method we can safely recommend using when performing batch correction of scRNA-seq data.
Lossless Single-Molecule Counting To Absolute Quantify Proteoforms

A novel immunoassay, termed PICO, is introduced to deliver unequivocal, reference-free quantification of proteoforms - absolute qunatification. PICO utilizes a fundamentally novel compartmentalised, homogenous single-molecule assay incorporating a lossless and utmost sensitive signal generation, down to a few molecules per reaction, and a background-free, digital enumeration principle, the decouplexing. PICO is presented as exact mathematical theories, providing a theoretical comprehension of its chemistry, enabling experimental control. Consequently, PICO permits absolute quantification, as demonstrated against recombinant and non-recombinant ErbB2 and multi-tagged peptide rTRX targets, validating absolute quantification against internal and external references in both analytical and cellular matrices. Furthermore, PICO enables combinatorial multiplexing (CMx), a readout between any two antibodies, demonstrated by an 8-plex antibody, 12-CMx PICO measuring functional changes of ErbB pathway upon dactolisib treatment delivering absolute quantitive cellular stoichiometry. PICO possesses immense potential for versatile, standardized, and accurate protein measurements, offering insights into physiological and perturbed cellular processes.
The role of rat prelimbic cortex in decision making

The frontal cortex plays a critical role in decision-making. One specific frontal area, the anterior cingulate cortex, has been identified as crucial for setting a threshold for how much evidence is needed before a choice is made (Domenech & Dreher, 2010). Threshold is a key concept in drift diffusion models, a popular framework used to understand decision-making processes. Here, we investigated the role of the prelimbic cortex, part of the rodent cingulate cortex, in decision making. Male and female rats learned to choose between stimuli associated with high and low value rewards. Females learned faster, were more selective in their responses, and integrated information about the stimuli more quickly. By contrast, males learned more slowly and showed a decrease in their decision thresholds during choice learning. Inactivating the prelimbic cortex in female and male rats sped up decision making without affecting choice accuracy. Drift diffusion modeling found selective effects of prelimbic cortex inactivation on the decision threshold, which was reduced with increasing doses of the GABA-A agonist muscimol. Stimulating the prelimbic cortex through mu opioid receptors slowed the animals’ choice latencies and increased the decision threshold. These findings provide the first causal evidence that the prelimbic cortex directly influences decision processes. Additionally, they suggest possible sex-based differences in early choice learning.
Volume based anomaly detection using LRD analysis of decomposed network traffic

Network traffic intrusions increase day by day in computer systems. This poses major security threats to computer networks. In this paper, we present an effective approach for anomaly detection in network traffic. We investigate the long-range dependence (LRD) behavior of decomposed network traffic subgroups in different directions with respect the enterprise network. If the network traffic exhibits LRD behavior during normal conditions, then deviation from this property can indicate an abnormality in the traffic. We analyze and evaluate recent Internet traffic captured at King Saud University (KSU). The results and analysis of the proposed approach show that the presence of short duration anomalies affect the LRD behavior of certain traffic subgroups, namely the subgroups in the control plane traffic while the aggregated whole traffic still exhibits LRD. These results show how this approach significantly reduces the amount of traffic to analyze, and more importantly it can detect abnormal behavior that is not detected when looking the traffic as a whole.
An Eventual α Partition-Participant Detector for MANETs

With Mobile Ad hoc Networks (MANETs), communication between mobile users is possible without any infrastructure. MANETs are already a necessary part of wireless systems. Due to arbitrary node arrivals, departures, crashes and movements, network partitioning may arise resulting in a degradation of the service, but not necessarily in its interruption. In this paper, we propose a distributed system model for partitionable systems. We then specify and implement a partition-participant detector that captures the liveness of a partition even if the partition is not completely stable. Its role is to detect the minimal stability condition to guarantee that eventually all the stable processes in an α-Set elect the same leader. Keywords-MANETs; models for dynamic partitionable systems; partitions; partition-participant detector; stability condition.
Genomic insights on the potential role of the accessory genome in the emergence of a novel geographically restricted K. pneumoniae lineage as a high-risk clone

Klebsiella pneumoniae causes life-threatening nosocomial infections and is featured by a remarkable propensity for multidrug resistance acquisition. Infections caused by multidrug- (MDR) and extensively drug-resistant (XDR) strains lead to a limitation of therapeutic options and an increase in persistent infections, and they are usually represented by high-risk lineages. Based on these features and their relevance to global public health, most of the studies focused on such high-risk clones, and little is known about the epidemiological and evolutionary dynamics of new/geographically restricted lineages. This study aimed to unveil the antimicrobial resistance and virulence genetic repertoire of a clinical XDR K. pneumoniae (Kp199) strain belonging to geographically-restricted ST, not linked to any known clonal complex. Its intrinsic (gyrA, parC, ramR, soxR and soxS mutations) and acquired resistome agreed with the observed XDR phenotype. An extensive arsenal of both antibiotic and heavy metal resistance genes was observed, as well as genes involved with resistance to several antiseptics currently used in clinical settings. The co-occurrence of blaKPC-2 and blaNDM-1 carbapenemase genes in Kp199 was an alarming finding since it could contribute to increased carbapenem resistance. Kp199 virulome was associated with bacterial survival and replication during infections. This study raises concern about a novel, geographically restricted K. pneumoniae lineage harbouring a huge resistome and virulome, which may strongly contribute to its successful establishment as an epidemic lineage. Therefore, our findings underscore the importance of vigilant surveillance and control measures to mitigate the threat posed by the potential emergence of new high-risk pandemic clones.
Characteristics and Outcome After Hospitalization for Acute Right Heart Failure in Patients With Pulmonary Arterial Hypertension

Background— Although much is known about the risk factors for poor outcome in patients hospitalized with acute heart failure and left ventricular dysfunction, much less is known about the syndrome of acute heart failure primarily affecting the right ventricle (acute right heart failure). Methods and Results— By using Stanford Hospital's pulmonary hypertension database, we identified consecutive acute right heart failure hospitalizations in patients with PAH. We used longitudinal regression analysis with the generalized estimating equations method to identify factors associated with an increased likelihood of 90-day mortality or urgent transplantation. From June 1999 to September 2009, 119 patients with PAH were hospitalized for acute right heart failure (207 episodes). Death or urgent transplantation occurred in 34 patients by 90 days of admission. Multivariable analysis identified a higher respiratory rate on admission (>20 breaths per minute; OR, 3.4; 95% CI, 1.5–7.8), renal dysfunction on admission (glomerular filtration rate <45 mL/min per 1.73 m2; OR, 2.7; 95% CI, 1.2–6.3), hyponatremia (serum sodium ⩽136 mEq/L; OR, 3.6; 95% CI, 1.7–7.9), and tricuspid regurgitation severity (OR, 2.5 per grade; 95% CI, 1.2–5.5) as independent factors associated with an increased likelihood of death or urgent transplantation. Conclusions— These results highlight the high mortality after hospitalizations for acute right heart failure in patients with PAH. Factors identifiable within hours of hospitalization may help predict the likelihood of death or the need for urgent transplantation in patients with PAH.
Journal of Graph Algorithms and Applications Morphing Planar Graph Drawings with Bent Edges

We give an algorithm to morph between two planar drawings of a graph, preserving planarity, but allowing edges to bend during the course of the morph. The morph is polynomial size and discrete: it uses a polynomial number of elementary steps, where each elementary step is a linear morph that moves each vertex along a straight line at uniform speed. Although there are previously-known planarity-preserving morphs that do not require edge bends, it is an open problem to find polynomial-size discrete morphs. We achieve polynomial size at the expense of edge bends.
Reconfigurable Interconnect Synthesis via Quantified Boole an Satisfiability

Many state-of-the-art reconfigurable computers consist of multiple field-programmableprocessors connected through a programmable interconnect fabric. Interconnect synthes is is the process of configuring the interconnection network to match the communication requirements of the designs impl e ented on the processors. We formulate interconnect synthesis as the problem of determining satisfiability of qu antified Boolean formulae. For this class of quantified Boolean formulae, we propose an efficient method to eliminat e the quantifiers so that satisfiability of the resulting quantifier-free formulae can be determined using ordinary s tisfiability solvers.
Single-grain Si thin-film transistors for analog and RF circuit applications

Single-grain (SG) Si-TFTs fabricated inside a location-controlled grain have SOI-like performance. To validate their potential for circuit application, key analog and RF building blocks are characterized. An operational amplifier (Opamp) and a voltage reference (Vref) demonstrate DC gain of 50 dB and power supply rejection ratio (PSRR) of 50 dB, respectively. With fT in the GHz range, SG-TFTs enable RF circuit design below 1 GHz. An RF cascode amplifier circuit is demonstrated.
Integrating Visual and Range Data for Robotic Object Detection

The problem of object detection and recognition is a notoriously difficult one, and one that has been the focus of much work in the computer vision and robotics communities. Most work has concentrated on systems that operate purely on visual inputs (i.e., images) and largely ignores other sensor modalities. However, despite the great progress made down this track, the goal of high accuracy object detection for robotic platforms in cluttered real-world environments remains elusive. Instead of relying on information from the image alone, we present a method that exploits the multiple sensor modalities available on a robotic platform. In particular, our method augments a 2-d object detector with 3-d information from a depth sensor to produce a “multi-modal object detector. ” We demonstrate our method on a working robotic system and evaluate its performance on a number of common household/office objects.
A QPTAS for TSP with Fat Weakly Disjoint Neighborhoods in Doubling Metrics

We consider the Traveling Salesman Problem with Neighborhoods (TSPN) in doubling metrics. The goal is to find a shortest tour that visits each of a collection of n subsets (regions or neighborhoods) in the underlying metric space. We give a quasi-polynomial time approximation scheme (QPTAS) when the regions are what we call α-fat weakly disjoint. This notion combines the existing notions of diameter variation, fatness and disjointness for geometric objects and generalizes these notions to any arbitrary metric space. Intuitively, the regions can be grouped into a bounded number of types, where in each type, the regions have similar upper bounds for their diameters, and each such region can designate a point such that these points are far away from one another.Our result generalizes the polynomial time approximation scheme (PTAS) for TSPN on the Euclidean plane by Mitchell (in SODA, pp. 11–18, 2007) and the QPTAS for TSP on doubling metrics by Talwar (in 36th STOC, pp. 281–290, 2004). We also observe that our techniques directly extend to a QPTAS for the Group Steiner Tree Problem on doubling metrics, with the same assumption on the groups.
A Reactive Scheme for Traffic Engineering in MPLS Networks

In this paper we develop a new Traffic Engineering scheme for congestion control in MPLS networks based on a reactive mechanism. While most existing TE schemes to prevent network congestion rely on constraint-based routing (CBR), the proposed algorithm uses a local search technique where the basic move is the modification of the route for a single Label Switched Path (LSP). Two versions of the algorithm are proposed: in the first one, called FID, an already established LSP is rerouted when a certain level of network congestion is detected, while in the second, called LFID, it is rerouted when a new LSP request cannot be satisfied. Experiments under a dynamic traffic scenario show a reduced rejection probability especially with long-lived and bandwidth consuming connection requests, thus proving a better network resource utilization compared to existing CBR schemes in MPLS networks.
asso-Effects of feeding a quebracho–chestnut tannin extract on lactating cow performance and nitrogen utilization efficiency

The effects of feeding a quebracho–chestnut tannin extract mixture on performance and nitrogen (N) utilization were assessed with 36 multiparous lactating Holstein cows (mean ± standard deviation; 706 ± 59 kg of body weight; 126 ± 20 d in milk) randomly assigned to 3 dietary treatments in a randomized complete block design. Following a 2-wk covariate adjustment period, cows were fed their assigned treatment diets for 13 wk. Rice hulls were removed from a total mixed ration with a 54:46 forage: concentrate ratio (% of dry matter; DM), and a tannin extract mixture from quebracho and chestnut trees (2:1 ratio) was included at 0, 0.45, and 1.80% of dietary DM. There was no interaction between dietary treatments and experimental week for the reported measurements except milk lactose percentage. Overall, treatments did not affect milk yield (48.6 ± 7.8 kg/d), fat-and protein-corrected milk (46.1 ± 7.6 kg/d), milk fat content (3.88 ± 0.65%) and yield (1.85 ± 0.38 kg/d), and true protein yield (1.45 ± 0.21 kg/d). However, incremental levels of tannin extracts in the diet produced a linear increase in DM intake (29.2 to 30.9 kg/d) and a linear decrease in kilograms of milk per kilogram of DM intake (1.67 to 1.57 kg/kg) and MUN (12.2 to 10.8 mg/dL). Furthermore, there was a quadratic effect of tannin extracts on milk true protein content (2.96, 3.13, and 3.00% for 0, 0.45, and 1.80% tannin extract, respectively) and a tendency for linear and quadratic response for body weight gain (0.31, 0.16, and 0.44 kg/d for 0, 0.45, and 1.80% tannin, respectively). Intake of N increased linearly (782, 795, and 820 g/d) and N utilization efficiency (milk N/ intake N) decreased linearly (0.300, 0.301, and 0.275 for 0, 0.45, and 1.80% tannin, respectively). Relative to the 0% diet, 1.80% tannin extract reduced estimated urinary N excretion by 11%. In this study, adding 0.45% tannin extract to the diet reduced feed efficiency but had a positive effect on milk protein content. Feeding a tannin extract mixture from quebracho and chestnut may reduce environmental labile urinary N excretion without affecting milk yield but at the expense of a lower feed utilization efficiency.
Cell Death Inhibiting RNA (CDIR) Derived from a 3′-Untranslated Region Binds AUF1 and Heat Shock Protein 27*

Regulators of programmed cell death were previously identified using a technical knockout genetic screen. Among the elements that inhibited interferon-γ-induced apoptosis of HeLa cells was a 441-nucleotide fragment derived from the 3′-untranslated region (UTR) of KIAA0425, a gene of unknown function. This fragment was termed cell death inhibiting RNA (CDIR). Deletion and mutation analyses of CDIR were employed to identify the features required for its anti-apoptotic activity. Single nucleotide alterations within either copy of the duplicated U-rich motif found in the CDIR sequence abolished the anti-apoptotic activity of CDIR and altered its in vitro association with a protein complex. Further analysis of the CDIR-binding complex indicated that it contained heat shock protein 27 (Hsp27) and the regulator of mRNA turnover AUF1 (heterogeneous nuclear ribonucleoprotein D). In addition, recombinant AUF1 bound directly to CDIR. Furthermore, expression of another AUF1-binding RNA element, derived from the 3′-UTR of c-myc, inhibited apoptosis. We also demonstrate that the level and the stability of p21 waf1/Cip1/sdi1 mRNA, a target of AUF1 with anti-apoptotic activity, were increased in CDIR-transfected cells. The level of mRNA and protein ofBcl-2, another anti-apoptotic gene, containing an AUF1 binding site in its 3′-UTR was also increased in CDIR-transfected cells. Our data suggest that AUF1 regulates apoptosis by altering mRNA turnover. We propose that CDIR inhibits apoptosis by acting as a competitive inhibitor of AUF1, preventing AUF1 from binding to its targets.
Inhibition of Epidermal Growth Factor Receptor-associated Tyrosine Kinase Blocks Glioblastoma Invasion of the Brain.

OBJECTIVE : Glioblastoma multiforme is a malignant primary brain tumor associated with short patient survival despite aggressive treatment, in part because of its propensity to aggressively infiltrate into brain tissue. Glioblastoma multiforme is also unique because it is the only nonepithelial human tumor for which excessive activation of epidermal growth factor receptor (EGFR) has been consistently linked to tumor growth and patient survival, and EGFR activation promotes glioblastoma multiforme infiltration in vitro. METHODS : Cocultures of human glioblastoma spheroids (derived from three separate patients) and fetal rat brain aggregates were examined for infiltration using confocal microscopy, in the presence of 0 to 100 nmol/L genistein, a tyrosine kinase (TK) inhibitor, and 3 nmol/L tyrphostin A25, a specific EGFR-TK inhibitor. RESULTS : Infiltration (not attachment) was completely inhibited by genistein at 10 nmol/L, the IC 20 for monolayer growth inhibition in two cell lines. Tyrphostin A25 at 3 nmol/L (the IC 20 for monolayers) reduced invasion in a third cell line from 38.8 ± 6.1% invasion-hour per hour (n = 5) to 2.9 ± 1.2% invasion-hour per hour (n = 6) (P = 0.0002, two-tailed t test, 93% inhibition), and from 0.54 ± 0.065% per hour (slope) to 0.028 ± 0.018% per hour (P = 0.00001, 95% inhibition). Maximal percent invasion was reduced from 100 ± 0 to 7.4 ± 5.6% of the fetal rat brain aggregate. No change was detected in EGFR-associated tyrosine phosphorylation at those doses in monolayers by 32 P immunolabeling, consistent with the known effects of low concentrations of TK inhibitors. An increase in expression of wild-type and truncated EGFR was demonstrated by Western blotting. Invasion was equally well inhibited by a monoclonal antibody to the high-affinity ligand binding domain of EGFR and not by antibody to an inactive domain. CONCLUSION : Our observations support the role of EGFR activation as a determinant by which glioblastoma invades normal brain tissue, and we show that invasion can be effectively inhibited at much lower concentrations of TK inhibitors than are necessary for growth suppression.
Author's Personal Copy Statistics and Probability Letters Consistency of Random Survival Forests

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t We prove uniform consistency of Random Survival Forests (RSF), a newly introduced forest ensemble learner for analysis of right-censored survival data. Consistency is proven under general splitting rules, bootstrapping, and random selection of variables—that is, under true implementation of the methodology. Under this setting we show that the forest ensemble survival function converges uniformly to the true population survival function. To prove this result we make one key assumption regarding the feature space: we assume that all variables are factors. Doing so ensures that the feature space has finite cardinality and enables us to exploit counting process theory and the uniform consistency of the Kaplan–Meier survival function.
Study of Adaptive Nonlinear Signal Processing Structures for Underwater Acoustic Systems

This takes a look at and explores the use of adaptive nonlinear sign processing structures for underwater acoustic systems. The focus of the research is on how such structures can improve the accuracy of acoustic alerts underwater by adapting to various environmental conditions. Particularly, the research will analyze various acoustic sign distortion additives caused by nonlinear distortion, as well as the effectiveness of diverse adaptive nonlinear signal processing systems for reducing these effects. Sign processing strategies will include both adaptive thresholding and adaptive nonlinear time-frequency transformations. The studies will examine the overall performance of these systems in phrases of signal fidelity, in addition to the impact on cognitive complexity. The effects of the studies will inform capability enhancements and packages of nonlinear sign processing systems for underwater acoustic structures.