Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic Response in Patients with Advanced Malignancies Treated with Combined Dendritic Cell–Activated T Cell Based Immunotherapy and Intensity–Modulated Radiotherapy

Successful cancer immunotherapy is confounded by the magnitude of the tumor burden and the presence of immunoregulatory elements that suppress an immune response. To approach these issues, 26 patients with advanced treatment refractory cancer were enrolled in a safety/feasibility study wherein a conventional treatment modality, intensity modulated radiotherapy (IMRT), was combined with dendritic cell-based immunotherapy. We hypothesized that radiation would lower the tumor burdens, decrease the number/function of regulatory cells in the tumor environment, and release products of tumor cells that could be acquired by intratumoral injected immature dendritic cells (iDC). Metastatic lesions identified by CT (computed tomography) were injected with autologous iDC combined with a cytokine-based adjuvant and KLH (keyhole limpet hemocyanin), followed 24 h later by IV-infused T-cells expanded with anti-CD3 and IL-2 (AT). After three to five days, each of the injected lesions was treated with fractionated doses of IMRT followed by another injection of intratumoral iDC and IV-infused AT. No toxicity was observed with cell infusion while radiation-related toxicity was observed in seven patients. Five patients had progressive disease, eight demonstrated complete resolution at treated sites but developed recurrent disease at other sites, and 13 showed complete response at various follow-up times with an overall estimated Kaplan-Meier disease-free survival of 345 days. Most patients developed KLH antibodies supporting our hypothesis that the co-injected iDC are functional with the capacity to acquire antigens from their environment and generate an adaptive immune response. These results demonstrate the safety and effectiveness of this multimodality strategy combining immunotherapy and IMRT in patients with advanced malignancies.
The Mitochondrial Voltage-Dependent Anion Channel 1, Ca2+ Transport, Apoptosis, and Their Regulation

In the outer mitochondrial membrane, the voltage-dependent anion channel 1 (VDAC1) functions in cellular Ca2+ homeostasis by mediating the transport of Ca2+ in and out of mitochondria. VDAC1 is highly Ca2+-permeable and modulates Ca2+ access to the mitochondrial intermembrane space. Intramitochondrial Ca2+ controls energy metabolism by enhancing the rate of NADH production via modulating critical enzymes in the tricarboxylic acid cycle and fatty acid oxidation. Mitochondrial [Ca2+] is regarded as an important determinant of cell sensitivity to apoptotic stimuli and was proposed to act as a “priming signal,” sensitizing the organelle and promoting the release of pro-apoptotic proteins. However, the precise mechanism by which intracellular Ca2+ ([Ca2+]i) mediates apoptosis is not known. Here, we review the roles of VDAC1 in mitochondrial Ca2+ homeostasis and in apoptosis. Accumulated evidence shows that apoptosis-inducing agents act by increasing [Ca2+]i and that this, in turn, augments VDAC1 expression levels. Thus, a new concept of how increased [Ca2+]i activates apoptosis is postulated. Specifically, increased [Ca2+]i enhances VDAC1 expression levels, followed by VDAC1 oligomerization, cytochrome c release, and subsequently apoptosis. Evidence supporting this new model suggesting that upregulation of VDAC1 expression constitutes a major mechanism by which apoptotic stimuli induce apoptosis with VDAC1 oligomerization being a molecular focal point in apoptosis regulation is presented. A new proposed mechanism of pro-apoptotic drug action, namely Ca2+-dependent enhancement of VDAC1 expression, provides a platform for developing a new class of anticancer drugs modulating VDAC1 levels via the promoter and for overcoming the resistance of cancer cells to chemotherapy.
Predicting the intended motion with EMG signals for an exoskeleton orthosis controller

In this paper, we present a method to calculate the intended motion of joints in the human body by analysing EMG signals. Those signals are emitted by the muscles attached to the adjoining bones during their activation. With the resulting intended motion, a leg orthosis can be controlled in realtime to support disabled people while walking or climbing stairs and help patients suffering from the effects of a stroke in their rehabilitation efforts. To allow a variety of different motions, a human body model with physical properties is developed and synchronized with data recorded from the pose sensors. Computing the intended motion is performed by converting calibrated EMG signals to muscle forces which animate the model. The algorithm was evaluated with experiments showing the calculated intended motion while climbing one step of a stair. The algorithm and the experimental results are both shown.
Childhood leukemia, electric and magnetic fields, and temporal trends

During the past 25 years concern has been raised about the possible health effects of extremely low frequency (ELF) electric and magnetic fields (EMFs), particularly regarding childhood leukemia. Comparison of changes in electricity consumption (a surrogate for exposure) to changes in childhood‐leukemia rates, known as ecologic correlation, have been used to argue both for and against the association between magnetic fields and childhood leukemia. In this paper we explore what can be learned from such an ecologic approach. We first examine separately the evidence on trends in exposure to EMFs and on trends in leukemia rates, and then compare the two. Both incidence rates and exposures have increased, but there are so many approximations and assumptions involved in connecting the two trends that we cannot regard the ecologic evidence as providing any meaningful evidence for or against a causal link. Bioelectromagnetics 27:545–552, 2006. © 2006 Wiley‐Liss, Inc.
Human Body Pose Estimation with Particle Swarm Optimisation

In this paper we address the problem of human body pose estimation from still images. A multi-view set of images of a person sitting at a table is acquired and the pose estimated. Reliable and efficient pose estimation from still images represents an important part of more complex algorithms, such as tracking human body pose in a video sequence, where it can be used to automatically initialise the tracker on the first frame. The quality of the initialisation influences the performance of the tracker in the subsequent frames. We formulate the body pose estimation as an analysis-by-synthesis optimisation algorithm, where a generic 3D human body model is used to illustrate the pose and the silhouettes extracted from the images are used as constraints. A simple test with gradient descent optimisation run from randomly selected initial positions in the search space shows that a more powerful optimisation method is required. We investigate the suitability of the Particle Swarm Optimisation (PSO) for solving this problem and compare its performance with an equivalent algorithm using Simulated Annealing (SA). Our tests show that the PSO outperforms the SA in terms of accuracy and consistency of the results, as well as speed of convergence.
Neurotrophins and spinal circuit function

Work early in the last century emphasized the stereotyped activity of spinal circuits based on studies of reflexes. However, the last several decades have focused on the plasticity of these spinal circuits. These considerations began with studies of the effects of monoamines on descending and reflex circuits. In recent years new classes of compounds called growth factors that are found in peripheral nerves and the spinal cord have been shown to affect circuit behavior in the spinal cord. In this review we will focus on the effects of neurotrophins, particularly nerve growth factor (NGF), brain derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3), on spinal circuits. We also discuss evidence that these molecules can modify functions including nociceptive behavior, motor reflexes and stepping behavior. Since these substances and their receptors are normally present in the spinal cord, they could potentially be useful in improving function in disease states and after injury. Here we review recent findings relevant to these translational issues.
Temperature Effect on Heavy-Ion Induced Parasitic Current on SRAM by Device Simulation: Effect on SEU Sensitivity

A temperature dependence analysis of single event transient currents induced by heavy-ions using TCAD simulation is performed in the 218 to 418 K range on a 0.18 mum SRAM cell manufactured by ATMEL. The single event upset (SEU) phenomena depends on both the heavy-ions-induced transient current and the technology. Temperature is shown to have a significant impact on the trends of heavy-ions-induced current. The SEU sensitivity is then expected to exhibit a large temperature dependence. However, a small influence on the SEU sensitivity is reported in this work on the studied technology.
SOCS1 regulates senescence and ferroptosis by modulating the expression of p53 target genes

The mechanism by which p53 suppresses tumorigenesis remains poorly understood. In the context of aberrant activation of the JAK/STAT5 pathway, SOCS1 is required for p53 activation and the regulation of cellular senescence. In order to identify p53 target genes acting during the senescence response to oncogenic STAT5A, we characterized the transcriptome of STAT5A-expressing cells after SOCS1 inhibition. We identified a set of SOCS1-dependent p53 target genes that include several secreted proteins and genes regulating oxidative metabolism and ferroptosis. Exogenous SOCS1 was sufficient to regulate the expression of p53 target genes and sensitized cells to ferroptosis. This effect correlated with the ability of SOCS1 to reduce the expression of the cystine transporter SLC7A11 and the levels of glutathione. SOCS1 and SOCS1-dependent p53 target genes were induced during the senescence response to oncogenic STAT5A, RasV12 or the tumor suppressor PML. However, while SOCS1 sensitized cells to ferroptosis neither RasV12 nor STAT5A mimicked the effect. Intriguingly, PML turned cells highly resistant to ferroptosis. The results indicate different susceptibilities to ferroptosis in senescent cells depending on the trigger and suggest the possibility of killing senescent cells by inhibiting pathways that mediate ferroptosis resistance.
Studies of Structure Formation and Cosmology with Galaxy Cluster Surveys

Surveys of galaxy clusters provide a promising method of testing models of structure formation in the universe. Within the context of our standard structure formation scenario, surveys provide measurements of the geometry of the universe and the nature of the dark energy and dark matter. Cluster catalogues will be constructed using some combination of X-ray, optical/near--IR, and mm or cm-wave observations. These catalogues will be used to study the cluster redshift and mass distributions along with the correlations of the cluster spatial distribution. These measurements probe the volume--redshift relation, the power spectrum of density fluctuations and the evolution of galaxy cluster abundance. All are sensitive to the amount of dark matter Omega_M, the amount of dark energy Omega_E, the equation of state of the dark energy w(z) and any other parameter, which affects the expansion history of the universe.
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning

In recent years, neural networks have proven to be effective in Chinese word segmentation. However, this promising performance relies on large-scale training data. Neural networks with conventional architectures cannot achieve the desired results in low-resource datasets due to the lack of labelled training data. In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains. Our framework consists of two parts, domain-based models and deep stacking networks. The domain-based models are used to learn knowledge from different datasets. The deep stacking networks are designed to integrate domain-based models. To reduce model conflicts, we innovatively add communication paths among models and design various structures of deep stacking networks, including Gaussian-based Stacking Networks, Concatenate-based Stacking Networks, Sequence-based Stacking Networks and Tree-based Stacking Networks. We conduct experiments on six low-resource datasets from various domains. Our proposed framework shows significant performance improvements on all datasets compared with several strong baselines.
Developing a Big Data-Enabled Transformation Model in Healthcare: A Practice Based View

Healthcare organizations are looking for opportunities to create new business model and value that can be implemented through information technology (IT) enabled transformation. Big data, an overwhelming phenomenon which has been addressed through various new and old data management technologies, hold the key to healthcare transformation. To address this, we developed a big-data-enabled transformation model based on practice-based view showing that the relationships among big data capability, big-data-enabled transformation practice, benefit dimensions, and firm performance. We tested this model by analyzing secondary data regarding big data in the healthcare context. Our results not only conceptually defined four big data capabilities but also found two significant path-to-performance chains. The contributions of this study are twofold. For management research, we establish a big-data-enabled transformation model to explain how big data leads to firm performance. For practitioners, we identify potential patterns that will help understanding big data's potentials and capabilities.
Recent Lessons Learned From Prevention and Recent-Onset Type 1 Diabetes Immunotherapy Trials

Type 1 diabetes (T1D) results from the immune system’s misguided attack on insulin-producing pancreatic β-cells, leading to lifelong insulin replacement therapy as well as to the risk for developing disease-associated complications (1–3). Over the past 2–3 decades, the field of clinical research in T1D has seen tremendous growth, including evaluation of a variety of promising immunotherapy approaches for the prevention or reversal of the disorder (4–6). In just the past 2 years, data from >10 trials have been reported, some revealing promising phase II results. However, phase III trials have failed to demonstrate efficacy. In light of these results, an anxiety-provoked question has arisen: Where does the field go from here? To this end, this article presents and elaborates on key emerging questions and recommendations for future immunotherapy trials in T1D. If implemented successfully, such strategies could accelerate the development of therapies with tangible clinical benefit in T1D because they perhaps more appropriately address the complex nature of the disease. Nearly 30 years after the first immunotherapy clinical trials in type 1 diabetes (T1D), progress has been realized. This progress includes advancements in scientific knowledge (e.g., immune markers, metabolic testing, pathogenesis), the breadth of agents under investigation (Fig. 1), and how clinical trials are increasingly performed as part of major collaborative networks with uniform protocols often bolstered with mechanistic assays. However, shortcomings remain in demonstrating a degree of therapeutic efficacy for recent-onset T1D immunotherapies that is sufficiently robust in terms of risk/benefit to satisfy the requirements for drug registration and approval by regulatory agencies (i.e., Food and Drug Administration, European Medicines Agency). In 2011 and early 2012 after a number of phase I and II recent-onset clinical trials, a series of phase IIB and III recent-onset T1D trials reported their outcomes (7–10). In advance …
Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation

In this article we address the task of cross-lingual sentiment lexicon learning, which aims to automatically generate sentiment lexicons for the target languages with available English sentiment lexicons. We formalize the task as a learning problem on a bilingual word graph, in which the intra-language relations among the words in the same language and the inter-language relations among the words between different languages are properly represented. With the words in the English sentiment lexicon as seeds, we propose a bilingual word graph label propagation approach to induce sentiment polarities of the unlabeled words in the target language. Particularly, we show that both synonym and antonym word relations can be used to build the intra-language relation, and that the word alignment information derived from bilingual parallel sentences can be effectively leveraged to build the inter-language relation. The evaluation of Chinese sentiment lexicon learning shows that the proposed approach outperforms existing approaches in both precision and recall. Experiments conducted on the NTCIR data set further demonstrate the effectiveness of the learned sentiment lexicon in sentence-level sentiment classification.
Γ-graphs of Graphs

A set S ⊆ V is a dominating set of a graph G = (V,E) if every vertex in V −S is adjacent to at least one vertex in S. The domination number γ(G) of G equals the minimum cardinality of a dominating set S in G; we say that such a set S is a γ-set. In this paper we consider the family of all γ-sets in a graph G and we define the γgraph G(γ) = (V (γ), E(γ)) of G to be the graph whose vertices V (γ) correspond 1-to-1 with the γ-sets of G, and two γ-sets, say D1 and D2, are adjacent in E(γ) if there exists a vertex v ∈ D1 and a vertex w ∈ D2 such that v is adjacent to w and D1 = D2 − {w} ∪ {v}, or equivalently, D2 = D1−{v}∪{w}. In this paper we initiate the study of γ-graphs of graphs. 518 G.H. Fricke, S.M. Hedetniemi, S.T. Hedetniemi and .
A Method for Evaluation of Selected Quality Properties in Rejuvenation Systems using Markov Model

Software fault-tolerance techniques have been widely used in computing systems to achieve high level of quality. Rejuvenation, a modern software fault-tolerance technique, has attracted a large number of researchers in software engineering area. Evaluating the effectiveness and feasibility of this technique becomes extremely important in selecting, comparing and applying it in actual software systems. The study of importantquality-attributes is the scientific basis for assessing the performance of software fault-tolerance techniques. This paper presents availability, reliability, safety evaluation of rejuvenation systems. Derived mathematical relations between failure probabilities and modeling parameters enable us to gain a great deal of quantitative results. Availability; reliability; safety; software fault tolerance; rejuvenation; preventive maintenance
Staphylococcal Enterotoxin B as a Biological Weapon: Recognition, Management, and Surveillance of Staphylococcal Enterotoxin

One of the most important toxin threats in warfare or bioterrorism is Staphylococcal enterotoxin B (SEB), an incapacitating toxin. SEB had been considered and produced as an offensive biologic warfare agent. Staphylococcal enterotoxin B is a toxin associated with incidences of massive food poisoning. The bacteria that produce this toxin (SEB) are universally associated with man and other warm blooded mammals and their spheres of environmental influence include sewage and plumes. Staphylococcus aureus can readily be isolated from nose, armpits or anal swabs and about 50% of clinical isolates produce this toxin. Staphylococcal enterotoxin B is one of the superantigens capable of massive nonspecific activation of the immune system including a massive release of cytokines, such as interferon-gamma, interleukin-6 and tumor necrosis factor-alpha. Staphylococcal enterotoxin B is a potential agent of bioterrorism because of the ease of its production and dispersion, a delayed onset of symptoms, an ability to cause high morbidity and the difficulty in discerning between intentional intoxication and natural intoxication when a viable organism is the etiologic agent. This article presents a brief discussion on the recognition, management and surveillance of SEB, as well as the pathogenesis, clinical manifestation, diagnosis, and treatment of patients exposed to this toxin.
Symbiotic nitrogen fixation and phosphorus acquisition. Plant nutrition in a world of declining renewable resources.

Since 1960, the world's population has doubled to 6 billion people. During the next 40 years, the population is projected to increase and stabilize at 8 to 9 billion people (Table [I][1]). In the 1960s, agricultural specialists predicted that food production would not keep pace with the burgeoning
Mammogram content-based image retrieval based on malignancy classification

Content-based image retrieval (CBIR) technique is increasingly gaining research attention as a Computer Aided Diagnosis (CAD) approach for breast cancer diagnosis. This work discusses a novel feature modeling technique for CBIR systems based on classifier scores and standard statistical calculations on the same. Established textural and geometric features are initially used to represent medical characteristics, before being used to generate secondary features through classifier scoring using the Support Vector Machine and Quadratic Discriminant Analysis classifiers. The model is validated through a range of benchmarks, and is shown to perform competitively in comparison to similar works.
Nucleotide variation of regulatory motifs may lead to distinct expression patterns

MOTIVATION Current methodologies for the selection of putative transcription factor binding sites (TFBS) rely on various assumptions such as over-representation of motifs occurring on gene promoters, and the use of motif descriptions such as consensus or position-specific scoring matrices (PSSMs). In order to avoid bias introduced by such assumptions, we apply an unsupervised motif extraction (MEX) algorithm to sequences of promoters. The extracted motifs are assessed for their likely cis-regulatory function by calculating the expression coherence (EC) of the corresponding genes, across a set of biological conditions. RESULTS Applying MEX to all Saccharomyces cerevisiae promoters, followed by EC analysis across 40 biological conditions, we obtained a high percentage of putative cis-regulatory motifs. We clustered motifs that obtained highly significant EC scores, based on both their sequence similarity and similarity in the biological conditions these motifs appear to regulate. We describe 20 clusters, some of which regroup known TFBS. The clusters display different mRNA expression profiles, correlated with typical changes in the nucleotide composition of their relevant motifs. In several cases, a variation of a single nucleotide is shown to lead to distinct differences in expression patterns. These results are confronted with additional information, such as binding of transcription factors to groups of genes. Detailed analysis is presented for clusters related to MCB/SCB, STRE and PAC. In the first two cases, we provide evidence for different binding mechanisms of different clusters of motifs. For PAC-related motifs we uncover a new cluster that has so far been overshadowed by the stronger effects of known PAC motifs. SUPPLEMENTARY INFORMATION Supplementary data are available at http://adios.tau. ac.il/regmotifs and at Bioinformatics online.