Miracle-dz/UniFAR-FTUs huggingface dataset:
TIGIT and PD-1 dual checkpoint blockade enhances antitumor immunity and survival in GBM

ABSTRACT The use of inhibitory checkpoint blockade in the management of glioblastoma has been studied in both preclinical and clinical settings. TIGIT is a novel checkpoint inhibitor recently discovered to play a role in cancer immunity. In this study, we sought to determine the effect of anti-PD-1 and anti-TIGIT combination therapy on survival in a murine glioblastoma (GBM) model, and to elucidate the underlying immune mechanisms. Using mice with intracranial GL261-luc+ tumors, we found that TIGIT expression was upregulated on CD8+ and regulatory T cells (Tregs) in the brain compared to draining cervical lymph nodes (CLN) and spleen. We then demonstrated that treatment using anti-PD-1 and anti-TIGIT dual therapy significantly improved survival compared to control and monotherapy groups. The therapeutic effect was correlated with both increased effector T cell function and downregulation of suppressive Tregs and tumor-infiltrating dendritic cells (TIDCs). Clinically, TIGIT expression on tumor-infiltrating lymphocytes was shown to be elevated in patient GBM samples, suggesting that the TIGIT pathway may be a valuable therapeutic target. Expression of the TIGIT ligand, PVR, further portended a poor survival outcome in patients with low-grade glioma. We conclude that anti-TIGIT is an effective treatment strategy against murine GBM when used in combination with anti-PD-1, improving overall survival via modifications of both the T cell and myeloid compartments. Given evidence of PVR expression on human GBM cells, TIGIT presents as a promising immune therapeutic target in the management of these patients.
Scenario-Based Insider Threat Detection From Cyber Activities

An insider threat scenario refers to the outcome of a set of malicious activities caused by intentional or unintentional misuse of the organization’s systems, networks, data, and resources. Prevention of insider threat is difficult, since trusted partners of the organization are involved in it, who have authorized access to these confidential/sensitive resources. The state-of-the-art research on insider threat detection mostly focuses on developing unsupervised behavioral anomaly detection techniques with the objective of finding out anomalousness or abnormal changes in user behavior over time. However, an anomalous activity is not necessarily malicious that can lead to an insider threat scenario. As an improvement to the existing approaches, we propose a technique for insider threat detection from time-series classification of user activities. Initially, a set of single-day features is computed from the user activity logs. A time-series feature vector is next constructed from the statistics of each single-day feature over a period of time. The label of each time-series feature vector (whether malicious or nonmalicious) is extracted from the ground truth. To classify the imbalanced ground-truth insider threat data consisting of only a small number of malicious instances, we employ a cost-sensitive data adjustment technique that undersamples the nonmalicious class instances randomly. As a classifier, we employ a two-layered deep autoencoder neural network and compare its performance with other popularly used classifiers: random forest and multilayer perceptron. Encouraging results are obtained by evaluating our approach using the CMU Insider Threat Data, which is the only publicly available insider threat data set consisting of about 14-GB web-browsing logs, along with logon, device connection, file transfer, and e-mail log files. We observe that both deep autoencoder and random forest classifiers classify the data-adjusted time-series feature set with high precision, recall, and f-score. Although multilayer perceptron has a high recall, it suffers from a lower precision and f-score compared to the other two classifiers.
Efficacy and safety of immune checkpoint inhibitors in non-small cell lung cancer

ABSTRACT Immune checkpoint inhibitors (ICIs) are new therapeutic strategies for non-small cell lung cancer (NSCLC). We aimed to quantitatively evaluate the efficacy and safety of ICIs in NSCLC. Pubmed, Embase, Cochrane Library, and Web of Science were searched for randomized clinical trials comparing ICIs with control therapies in NSCLC. Data were pooled according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A total of 12 trails comprising 6,919 NSCLC patients were included in this meta-analysis. ICIs therapies significantly improved progression-free survival (PFS) (HR, 0.838; P < 0.001), overall survival (OS) (HR, 0.747; P < 0.001) and objective response rates (ORR) (RR, 1.311; P < 0.001) in NSCLC. Prognostic benefit was observed irrespective of age, sex, treatment line, performance status and histology. Survival improvement of ICIs was limited for NSCLC patients with non-smoker (PFS, P = 0.468; OS, P = 0.317) or central nervous system (CNS) metastasis (PFS, P = 0.209; OS, P = 0.090), or positive EGFR mutation (PFS, P = 0.083; OS, P = 0.522) or PD-L1 expression level less than 5% (PFS, P = 0.370; OS, P = 0.047). The relative risks of all-grade and high-grade (≥3) anemia, neutropenia, leukopenia, thrombocytopenia, stomatitis, nausea, pyrexia, asthenia and neuropathy were all decreased in patients received ICIs compared with control therapies. This meta-analysis provides clinical evidence that ICIs improve PFS, OS, and ORR in NSCLC with fewer adverse effects. Our data establish ICIs as a prefer treatment option for NSCLC patients with smoker, no CNS metastasis, wild type EGFR, and high PD-L1 expression.
Streptozotocin-induced diabetic mice exhibit reduced experimental choroidal neovascularization but not corneal neovascularization

The present study aimed to investigate the effects of diabetes mellitus (DM) on the generation of experimental corneal neovascularization (CrNV) and choroidal neovascularization (ChNV). Diabetes was induced in mice by intraperitoneal injection of streptozotocin (STZ). Experimental CrNV and ChNV were induced by alkali injury and laser photocoagulation, respectively. CrNV and ChNV were compared between the STZ-induced diabetic mice and control mice two weeks after injury. Relative expression of angiogenic factors was quantified by reverse transcription-quantitative polymerase chain reaction, and progenitor cell or macrophage accumulation in the early phase following injury was examined by flow cytometric analysis. Compared with the alkali-injured normal mice, the alkali-injured diabetic mice (STZ-induced) exhibited no significant difference in CrNV occurrence, whereas the laser-injured diabetic mice exhibited significantly reduced levels of ChNV compared with those of the laser-injured control animals. The laser-induced intrachoroidal mRNA expression levels of angiogenic factors, including vascular endothelial growth factor, hypoxia-induced factor-1α, chemokine (C-C motif) ligand 3, and stromal cell-derived factor-1α, were reduced in the laser-injured diabetic mice when compared with laser-injured control mice. Furthermore, the laser-induced intrachoroidal infiltration of c-Kit+ progenitor cells was impaired in the laser-injured diabetic mice compared with the laser-injured control mice. Overall, diabetes did not exert a significant effect on the generation of experimental CrNV. However, diabetes reduced laser-induced ChNV through downregulation of intrachoroidal progenitor cell infiltration and angiogenic factor expression.
BPE and computer-extracted parenchymal enhancement for breast cancer risk, response monitoring, and prognosis

Functional behavior of breast cancer - representing underlying biology - can be analyzed using MRI. The most widely used breast MR imaging protocol is dynamic contrast-enhanced T1-weighted imaging. The cancer enhances on dynamic contrast-enhanced MR imaging because the contrast agent leaks from the leaky vessels into the interstitial space. The contrast agent subsequently leaks back into the vascular space, creating a washout effect. The normal parenchymal tissue of the breast can also enhance after contrast injection. This enhancement generally increases over time. Typically, a radiologist assesses this background parenchymal enhancement (BPE) using the Breast Imaging Reporting and Data System (BI-RADS). According to the BI-RADS, BPE refers to the volume of enhancement and the intensity of enhancement and is divided in four incremental categories: minimal, mild, moderate, and marked. Researchers have developed semi-automatic and automatic methods to extract properties of BPE from MR images. For clarity, in this syllabus the BI-RADS definition will be referred to as BPE, whereas the computer-extracted properties will not. Both BPE and computer-extracted parenchymal enhancement properties have been linked to screening and diagnosis, hormone status and age, risk of development of breast cancer, response monitoring, and prognosis.
Efficient Rank Minimization via Solving Non-convexPenalties by Iterative Shrinkage-Thresholding Algorithm

Rank minimization (RM) is a wildly investigated task of finding solutions by exploiting low-rank structure of parameter matrices. Recently, solving RM problem by leveraging non-convex relaxations has received significant attention. It has been demonstrated by some theoretical and experimental work that non-convex relaxation, e.g. Truncated Nuclear Norm Regularization (TNNR) and Reweighted Nuclear Norm Regularization (RNNR), can provide a better approximation of original problems than convex relaxations. However, designing an efficient algorithm with theoretical guarantee remains a challenging problem. In this paper, we propose a simple but efficient proximal-type method, namely Iterative Shrinkage-Thresholding Algorithm(ISTA), with concrete analysis to solve rank minimization problems with both non-convex weighted and reweighted nuclear norm as low-rank regularizers. Theoretically, the proposed method could converge to the critical point under very mild assumptions with the rate in the order of $O(1/T)$. Moreover, the experimental results on both synthetic data and real world data sets show that proposed algorithm outperforms state-of-arts in both efficiency and accuracy.
Optimal Design of Point-Focusing Shear Vertical Wave Electromagnetic Ultrasonic Transducers Based on Orthogonal Test Method

The lower conversion efficiency is the main drawback of electromagnetic acoustic transducers (EMATs) compared with other transducers, which limits their use in many industrial applications. In this paper, for enhancing the performance of point-focusing shear vertical wave EMATs (PFSV-EMATs), 18 orthogonal tests with seven factors and three levels are conducted through finite-element simulations. First, the degree of seven factors on the signal amplitude, axial focus offset, and radial focus offset is obtained by range analysis and compared. It indicates that the effect of the liftoff distance and the exciting current on signal amplitude is the largest, while the effect of the inner radius of concentric meander-line coils on the axial and radial focal offset is the largest. Then, the influence degree of various factors on test results is analyzed at frequencies of 1.5, 2, and 2.5 MHz, showing that the relatively low frequency, i.e., the sparser coil spacing, is favorable for signal amplitude and detrimental for point-focusing ability. Finally, experiments are carried out to verify the validation of optimization results, which indicates that the increased signal amplitude of 88.78%, smaller axial focus offset, and zero radial focus offset can be achieved by the optimized PFSV-EMAT, which has the improved signal amplitude and point-focusing behavior, compared with the non-optimized one.
Resequencing core accessions of a pedigree identifies derivation of genomic segments and key agronomic trait loci during cotton improvement

Summary Upland cotton (Gossypium hirsutum) is the world's largest source of natural fibre and dominates the global textile industry. Hybrid cotton varieties exhibit strong heterosis that confers high fibre yields, yet the genome‐wide effects of artificial selection that have influenced Upland cotton during its breeding history are poorly understood. Here, we resequenced Upland cotton genomes and constructed a variation map of an intact breeding pedigree comprising seven elite and 19 backbone parents. Compared to wild accessions, the 26 pedigree accessions underwent strong artificial selection during domestication that has resulted in reduced genetic diversity but stronger linkage disequilibrium and higher extents of selective sweeps. In contrast to the backbone parents, the elite parents have acquired significantly improved agronomic traits, with an especially pronounced increase in the lint percentage. Notably, identify by descent (IBD) tracking revealed that the elite parents inherited abundant beneficial trait segments and loci from the backbone parents and our combined analyses led to the identification of a core genomic segment which was inherited in the elite lines from the parents Zhong 7263 and Ejing 1 and that was strongly associated with lint percentage. Additionally, SNP correlation analysis of this core segment showed that a non‐synonymous SNP (A‐to‐G) site in a gene encoding the cell wall‐associated receptor‐like kinase 3 (GhWAKL3) protein was highly correlated with increased lint percentage. Our results substantially increase the valuable genomics resources available for future genetic and functional genomics studies of cotton and reveal insights that will facilitate yield increases in the molecular breeding of cotton.
Conditioning sharpens the spatial representation of rewarded stimuli in mouse primary visual cortex

Reward is often employed as reinforcement in behavioral paradigms but it is unclear how the visuospatial aspect of a stimulus-reward association affects the cortical representation of visual space. Using a head-fixed paradigm, we conditioned mice to associate the same visual pattern in adjacent retinotopic regions with availability and absence of reward. Time-lapse intrinsic optical signal imaging under anesthesia showed that conditioning increased the spatial separation of mesoscale cortical representations of reward predicting- and non-reward predicting stimuli. Subsequent in vivo two-photon calcium imaging revealed that this improved separation correlated with enhanced population coding for retinotopic location, specifically for the trained orientation and spatially confined to the V1 region where rewarded and non-rewarded stimulus representations bordered. These results are corroborated by conditioning-induced differences in the correlation structure of population activity. Thus, the cortical representation of visual space is sharpened as consequence of associative stimulus-reward learning while the overall retinotopic map remains unaltered.
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms

The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.
Effects of Preview Time in Manual Tracking Tasks

In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict the human behavior adaptations offline, by finding the model parameters that yield optimal performance at each preview time. These predictions are then verified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects performed a tracking task with eight different preview time settings between 0 and 2 s. Results show that the tracking performance improves and the model's “look-ahead” time parameters increase with increasing preview time. Beyond a certain preview time, approximately 0.6 s and 1.15 s in single- and double-integrator tasks, respectively, additional preview evokes no further adaptations. The offline model predictions closely match the experimental results, which thereby promises to facilitate similar quantitative insights in other tasks with restricted preview.
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin

In this paper we describe a dataset of German and Latin \textit{ground truth} (GT) for historical OCR in the form of printed text line images paired with their transcription. This dataset, called \textit{GT4HistOCR}, consists of 313,173 line pairs covering a wide period of printing dates from incunabula from the 15th century to 19th century books printed in Fraktur types and is openly available under a CC-BY 4.0 license. The special form of GT as line image/transcription pairs makes it directly usable to train state-of-the-art recognition models for OCR software employing recurring neural networks in LSTM architecture such as Tesseract 4 or OCRopus. We also provide some pretrained OCRopus models for subcorpora of our dataset yielding between 95\% (early printings) and 98\% (19th century Fraktur printings) character accuracy rates on unseen test cases, a Perl script to harmonize GT produced by different transcription rules, and give hints on how to construct GT for OCR purposes which has requirements that may differ from linguistically motivated transcriptions.
Non-rhythmic head-direction cells in the parahippocampal region are not constrained by attractor network dynamics

Computational models postulate that head-direction (HD) cells are part of an attractor network integrating head turns. This network requires inputs from visual landmarks to anchor the HD signal to the external world. We investigated whether information about HD and visual landmarks is integrated in the medial entorhinal cortex and parasubiculum, resulting in neurons expressing a conjunctive code for HD and visual landmarks. We found that parahippocampal HD cells could be divided into two classes based on their theta-rhythmic activity: non-rhythmic and theta-rhythmic HD cells. Manipulations of the visual landmarks caused tuning curve alterations in most HD cells, with the largest visually driven changes observed in non-rhythmic HD cells. Importantly, the tuning modifications of non-rhythmic HD cells were often non-coherent across cells, refuting the notion that attractor-like dynamics control non-rhythmic HD cells. These findings reveal a new population of non-rhythmic HD cells whose malleable organization is controlled by visual landmarks.
Semi-Supervised Feature Selection via Insensitive Sparse Regression with Application to Video Semantic Recognition

Feature selection plays a significant role in dealing with high-dimensional data to avoid the curse of dimensionality. In many real applications, like video semantic recognition, handling few labeled and large unlabeled data samples from the same population is a recently addressed challenge in feature selection. To solve this problem, we propose a novel semi-supervised feature selection method via insensitive sparse regression (ISR). Specifically, we compute the soft label matrix by the special label propagation, which can predict the labels of the unlabeled data. To guarantee the robustness of ISR to the false labeled instances or outliers, we propose Insensitive Regression Model (IRM) by capped <inline-formula><tex-math notation="LaTeX">$l_2$</tex-math><alternatives> <inline-graphic xlink:href="hou-ieq1-2810286.gif"/></alternatives></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$l_p$</tex-math><alternatives> <inline-graphic xlink:href="hou-ieq2-2810286.gif"/></alternatives></inline-formula>-norm loss. The soft label is imposed as the weights of IRM to fully utilize the label information. Meanwhile, to perform feature selection, we incorporate <inline-formula><tex-math notation="LaTeX"> $l_{2,q}$</tex-math><alternatives> <inline-graphic xlink:href="hou-ieq3-2810286.gif"/></alternatives></inline-formula> -norm regularizer with IRM as the structural sparsity constraint when <inline-formula><tex-math notation="LaTeX"> $0<q\leq 1$</tex-math><alternatives> <inline-graphic xlink:href="hou-ieq4-2810286.gif"/></alternatives> </inline-formula>. Moreover, we put forward an effective approach for solving the formulated non-convex optimization problem. We analyze the performance of convergence rigorously and discuss the parameter determination problem. Extensive experimental results on several public data sets verify the effectiveness of our proposed algorithm in comparison with the state-of-art feature selection methods. Finally, we apply our method to video semantic recognition successfully.
Formulation development of self-nanoemulsifying drug delivery system of celecoxib for the management of oral cavity inflammation

Abstract The oral administration of celecoxib (CLX) is a real problem because of its low aqueous solubility that results in high variability in absorption and its severe adverse effect such as cardiotoxic effects and gastrointestinal toxicity. Self-nanoemulsifying drug delivery systems (SNEDDS) can enhance the poor dissolution and erratic absorption of poorly water-soluble drugs such as CLX. This study was conducted to investigate the potential of SNEDDS to enhance the efficacy of CLX on inflamed mucous tissue and reduce systemic adverse effects by increasing its poor dissolution properties. A pseudo-ternary phase diagram was derived from the results of CLX solubility experiments in various excipients. These studies revealed the use of Labrafil M 2515 CS as oil, tween 80 as a surfactant, and polyethylene glycol 400 as a co-surfactant for the optimization of SNEDDS formulations. Eight formulations were formulated and characterized by their particle size, polydispersity index, viscosity, globular shape, drug solubility, self-emulsification efficiency, in vitro drug release, and permeation. The anti-inflammatory effect of CLX-SNEDDS was evaluated by carrageenan-induced cheek oedema in rats. The cheeks were treated with CLX-SNEDDS before oedema induction and then noticed for narrow periods (2 h) followed by histopathological studies to determine the efficacy of treatment. The selected formulations (F3 and F5) showed spherical morphologies under transmission electron microscopy, mean droplet sizes of 116.9 ± 1.78 and 124 ± 1.87 nm, respectively, complete in vitro drug release, and high cumulative amounts of drug permeation in 8 h. They also showed significant remarkable cheek oedema inhibition in comparison with the control groups (p < 0.05). CLX-SNEDDS was found to achieve effective local therapeutic concentration and intended to reduce cheek oedema, congestive capillary, inflammatory cells, and side effects due to lower dose size.