Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of early response biomarkers in relation to long‐term survival in patients with HER2‐negative breast cancer receiving neoadjuvant chemotherapy plus bevacizumab: Results from the Phase II PROMIX trial

Pathologic complete response (pCR) is a predictor for favorable outcome after neoadjuvant treatment in early breast cancer. Modulation of gene expression may also provide early readouts of biological activity and prognosis, offering the possibility for timely response‐guided treatment adjustment. The role of early transcriptional changes in predicting response to neoadjuvant chemotherapy plus bevacizumab was investigated. One‐hundred‐and‐fifty patients with large, operable and locally advanced HER2‐negative breast cancer received epirubicin and docetaxel, with the addition of bevacizumab. Patients underwent tumor biopsies at baseline, after Cycle 2 and at the time of surgery. The primary end point, pCR, and its relation with the secondary endpoints event‐free survival (EFS), overall survival (OS) and gene expression profiles, are reported. The pCR rate was 13% (95% CI 8.6–20.2), with significantly more pCRs among triple‐negative [28% (95% CI 14.8–45.4)] than among hormone receptor positive (HR+) tumors [9% (95% CI 4.6–16.3); (OR = 3.9 [CI = 1.5–10.3])]. pCR rates were not associated with EFS or OS. PAM50 subtypes significantly changed after Cycle 2 (p = 0.03) and an index of absolute changes in PAM50 correlations between these time‐points was associated with EFS [HR = 0.62 (CI = 0.3–1.1)]. In univariable analyses, signatures for angiogenesis, proliferation, estrogen receptor signaling, invasion and metastasis, and immune response, measured after Cycle 2, were associated with pCR in HR+ tumors. Evaluation of changes in molecular subtypes and other signatures early in the course of neoadjuvant treatment may be predictive of pCR and EFS. These factors may help guide further treatment and should be considered when designing neoadjuvant trials.
BRASSINOSTEROID INSENSITIVE2 negatively regulates cellulose synthesis in Arabidopsis by phosphorylating cellulose synthase 1

Significance Cellulose is the most abundant biopolymer on Earth and is a critical component for plants to grow and develop. Cellulose is synthesized by large cellulose synthase complexes containing multiple cellulose synthase A (CESA) subunits; however, how cellulose synthesis is regulated remains unclear. In this study, we identify BRASSINOSTEROID INSENSITIVE2 (BIN2) as a protein kinase that directly phosphorylates Arabidopsis CESA1 and further demonstrate that this phosphorylation event negatively regulates CESA activity, and thus cellulose biosynthesis, in Arabidopsis. Therefore, this study provides a clear link between cell wall biosynthesis and hormonal signal transduction pathways that regulate plant growth and development. The deposition of cellulose is a defining aspect of plant growth and development, but regulation of this process is poorly understood. Here, we demonstrate that the protein kinase BRASSINOSTEROID INSENSITIVE2 (BIN2), a key negative regulator of brassinosteroid (BR) signaling, can phosphorylate Arabidopsis cellulose synthase A1 (CESA1), a subunit of the primary cell wall cellulose synthase complex, and thereby negatively regulate cellulose biosynthesis. Accordingly, point mutations of the BIN2-mediated CESA1 phosphorylation site abolished BIN2-dependent regulation of cellulose synthase activity. Hence, we have uncovered a mechanism for how BR signaling can modulate cellulose synthesis in plants.
Exercise Increases Mitochondrial PGC-1α Content and Promotes Nuclear-Mitochondrial Cross-talk to Coordinate Mitochondrial Biogenesis*

Endurance exercise is known to induce metabolic adaptations in skeletal muscle via activation of the transcriptional co-activator peroxisome proliferator-activated receptor γ co-activator 1α (PGC-1α). PGC-1α regulates mitochondrial biogenesis via regulating transcription of nuclear-encoded mitochondrial genes. Recently, PGC-1α has been shown to reside in mitochondria; however, the physiological consequences of mitochondrial PGC-1α remain unknown. We sought to delineate if an acute bout of endurance exercise can mediate an increase in mitochondrial PGC-1α content where it may co-activate mitochondrial transcription factor A to promote mtDNA transcription. C57Bl/6J mice (n = 12/group; ♀ = ♂) were randomly assigned to sedentary (SED), forced-endurance (END) exercise (15 m/min for 90 min), or forced endurance +3 h of recovery (END+3h) group. The END group was sacrificed immediately after exercise, whereas the SED and END+3h groups were euthanized 3 h after acute exercise. Acute exercise coordinately increased the mRNA expression of nuclear and mitochondrial DNA-encoded mitochondrial transcripts. Nuclear and mitochondrial abundance of PGC-1α in END and END+3h groups was significantly higher versus SED mice. In mitochondria, PGC-1α is in a complex with mitochondrial transcription factor A at mtDNA D-loop, and this interaction was positively modulated by exercise, similar to the increased binding of PGC-1α at the NRF-1 promoter. We conclude that in response to acute altered energy demands, PGC-1α re-localizes into nuclear and mitochondrial compartments where it functions as a transcriptional co-activator for both nuclear and mitochondrial DNA transcription factors. These results suggest that PGC-1α may dynamically facilitate nuclear-mitochondrial DNA cross-talk to promote net mitochondrial biogenesis.
An assessment of vulnerability of hardware neural networks to dynamic voltage and temperature variations

As a problem solving method, neural networks have shown broad applicability from medical applications, speech recognition, and natural language processing. This success has even led to implementation of neural network algorithms into hardware. In this paper, we explore two questions: (a) to what extent microelectronic variations affects the quality of results by neural networks; and (b) if the answer to first question represents an opportunity to optimize the implementation of neural network algorithms. Regarding first question, variations are now increasingly common in aggressive process nodes and typically manifest as an increased frequency of timing errors. Combating variations — due to process and/or operating conditions — usually results in increased guardbands in circuit and architectural design, thus reducing the gains from process technology advances. Given the inherent resilience of neural networks due to adaptation of their learning parameters, one would expect the quality of results produced by neural networks to be relatively insensitive to the rising timing error rates caused by increased variations. On the contrary, using two frequently used neural networks (MLP and CNN), our results show that variations can significantly affect the inference accuracy. This paper outlines our assessment methodology and use of a cross-layer evaluation approach that extracts hardware-level errors from twenty different operating conditions and then inject such errors back to the software layer in an attempt to answer the second question posed above.
A Service-Based Approach to Situational Correlation and Analyses of Stream Sensor Data

IoT service and service composition provide an effective means to develop IoT applications based on correlating multiple sensor data. The change of specific sensor data can cause others' changes under uncertain situations. It makes difficult for defining service composition plan in advance to build IoT application. This paper proposes a data-driven service composition method based on our previous proactive data service model. We regard service events frequently happen together with given service event as its situation, and the service events happen next as reacted actions under the situation. We analyze two kinds of correlation among service events via an improved FP-tree algorithm, and realize the service composition at runtime based on the real-time service events. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate that our method can effectively detect new service events based on current service events.
An Approach to Support the Specification of Agile Artifacts in the Development of Safety-Critical Systems

Providing a correct, complete, and unambiguous requirements specification is still one of the biggest challenges in software engineering. In the case of safety-critical systems, this challenge is even greater, since misinterpretations can lead to catastrophic damages to humans and to the environment. Agile development proposes minimizing the challenges in requirements specifications through short iterations, quick feedback, and active stakeholders. However, in safety-critical systems development, there is a gap (either geographical, cultural, educational, or temporal) between safety engineers and developers. [Question Problem] Therefore, it is not possible to be assured by agile development teams that safety aspects are well understood by developers, and, if the latter are aware of the criticality of the problem, that they can implement them accordingly. [Principal Ideas Results] The proposed research aims to provide adequate support for more accurate specification of agile development artifacts in the development of safety-critical systems. In this regard, the first contribution of this research aims at defining an Agile Safety Process, whose purpose is to identify which artifacts or parts thereof are enough to specify failure detection and containment, as well as measures for taking the system to a safe state. The second contribution aims at providing a semi-automated methodology for supporting the specification of agile artifacts, taking into account safety aspects. As a consequence, this research will have a significant impact in terms of improving the creation of evidences to be submitted for certification in terms of timing and accuracy.
Lyophilization of an Adjuvanted Mycobacterium tuberculosis Vaccine in a Single-Chamber Pharmaceutical Cartridge

Although substantial effort has been made in the development of next-generation recombinant vaccine systems, maintenance of a cold chain is still typically required and remains a critical challenge in effective vaccine distribution. The ability to engineer alternative containment systems that improve distribution and administration represents potentially significant enhancements to vaccination strategies. In this work, we evaluate the ability to successfully lyophilize a previously demonstrated thermostable tuberculosis vaccine formulation (ID93 + GLA-SE) in a cartridge format compared to a traditional vial container format. Due to differences in the shape of the container formats, a novel apparatus was developed to facilitate lyophilization in a cartridge. Following lyophilization, the lyophilizate was assessed visually, by determining residual moisture content, and by collecting melting profiles. Reconstituted formulations were assayed for particle size, protein presence, and GLA content. Based on assessment of the lyophilizate, the multicomponent vaccine was successfully lyophilized in both formats. Also, the physicochemical properties of the major components in the formulation, including antigen and adjuvant, were retained after lyophilization in either format. Ultimately, this study demonstrates that complex formulations can be lyophilized in alternative container formats to the standard pharmaceutical glass vial, potentially helping to increase the distribution of vaccines.
Optimizing Slate Recommendations via Slate-CVAE

The slate recommendation problem aims to find the "optimal" ordering of a subset of documents to be presented on a surface that we call "slate". The definition of "optimal" changes depending on the underlying applications but a typical goal is to maximize user engagement with the slate. Solving this problem at scale is hard due to the combinatorial explosion of documents to show and their display positions on the slate. In this paper, we introduce Slate Conditional Variational Auto-Encoders (Slate-CVAE) to generate optimal slates. To the best of our knowledge, this is the first conditional generative model that provides a unified framework for slate recommendation by direct generation. Slate-CVAE automatically takes into account the format of the slate and any biases that the representation causes, thus truly proposing the optimal slate. Additionally, to deal with large corpora of documents, we present a novel approach that uses pretrained document embeddings combined with a soft-nearest-neighbors layer within our CVAE model. Experiments show that on the simulated and real-world datasets, Slate-CVAE outperforms recommender systems that consists of greedily ranking documents by a significant margin while remaining scalable.
Genetically engineered mouse models in oncology research and cancer medicine

Genetically engineered mouse models (GEMMs) have contributed significantly to the field of cancer research. In contrast to cancer cell inoculation models, GEMMs develop de novo tumors in a natural immune‐proficient microenvironment. Tumors arising in advanced GEMMs closely mimic the histopathological and molecular features of their human counterparts, display genetic heterogeneity, and are able to spontaneously progress toward metastatic disease. As such, GEMMs are generally superior to cancer cell inoculation models, which show no or limited heterogeneity and are often metastatic from the start. Given that GEMMs capture both tumor cell‐intrinsic and cell‐extrinsic factors that drive de novo tumor initiation and progression toward metastatic disease, these models are indispensable for preclinical research. GEMMs have successfully been used to validate candidate cancer genes and drug targets, assess therapy efficacy, dissect the impact of the tumor microenvironment, and evaluate mechanisms of drug resistance. In vivo validation of candidate cancer genes and therapeutic targets is further accelerated by recent advances in genetic engineering that enable fast‐track generation and fine‐tuning of GEMMs to more closely resemble human patients. In addition, aligning preclinical tumor intervention studies in advanced GEMMs with clinical studies in patients is expected to accelerate the development of novel therapeutic strategies and their translation into the clinic.
Current status and future trends in ultrasonic transducers for medical imaging applications

Ultrasonic medical imaging provides a low cost and non-invasive tool to look inside the human body. The ultrasound transducer composed of various components including a piezoelectric material, and matching and backing layers, provides a link between the human body being imaged and the system electronics. Proper choice of the individual material components and the overall design of the transducer play an important role in the determining the final image quality. The basic design principles for fabrication of ultrasound transducers and their effect on the device performance are presented. Some recent advances in ultrasound, including 2-D arrays, 3-D image reconstruction, Doppler color flow imaging, and harmonic and contrast agent imaging have also been briefly described. Finally, the discovery of high coupling constant single crystal piezoelectric materials and their impact on the transducer performance are discussed.
A Comparison of Dictionary Building Methods for Sentiment Analysis in Software Engineering Text

Sentiment Analysis (SA) in Software Engineering (SE) texts suffers from low accuracies primarily due to the lack of an effective dictionary. The use of a domain-specific dictionary can improve the accuracy of SA in a particular domain. Building a domain dictionary is not a trivial task. The performance of lexical SA also varies based on the method applied to develop the dictionary. This paper includes a quantitative comparison of four dictionaries representing distinct dictionary building methods to identify which methods have higher/lower potential to perform well in constructing a domain dictionary for SA in SE texts.
Finite Element based Elastic-Plastic Contact Behaviour of a Sphere against a Rigid Flat - Effect of Strain Hardening

The present study considers an elastic-plastic contact analysis of a deformable sphere with a rigid flat using finite element method. The effect of strain hardening on the contact behaviour of a non-adhesive frictionless elastic-plastic contact is analyzed using commercial finite element software ANSYS. To study the strain hardening effect we have taken different values of tangent modulus. The result of strain hardening effect clearly shows that a generalized solution can not be applicable for all kind of materials as the effect of strain hardening differently influenced the contact parameters. With the increase in the value of hardening parameter this effect also increases. For higher value of hardening parameter the effect of strain hardening is severe on contact parameters. With the increase in strain hardening the resistance to deformation of a material is increased and the material becomes capable of carrying higher amount of load in a smaller contact area.
NFκB–Pim-1–Eomesodermin axis is critical for maintaining CD8 T-cell memory quality

Significance Mice and humans whose T cells are deficient in NFκB signaling lack memory T cells, but the mechanism behind this is unclear. We found that NFκB signaling is required during the resolution phase of the immune response to maintain long-term CD8 memory. NFκB signaling is necessary for preserving expression of Eomesodermin and prosurvival Bcl-2 in memory T cells, in a cell-intrinsic process where T-cell receptor (TCR) signals and Pim-1 kinase are involved. Our study defines an unexpected role of NFκB and Pim-1 signaling in the maintenance of T-cell memory quality. Furthermore, it identifies targets and specific times of intervention where protective T-cell memory could be reinforced in vaccines and cancer immunotherapies by manipulation of the NFκB–Pim-1–Eomesodermin axis. T-cell memory is critical for long-term immunity. However, the factors involved in maintaining the persistence, function, and phenotype of the memory pool are undefined. Eomesodermin (Eomes) is required for the establishment of the memory pool. Here, we show that in T cells transitioning to memory, the expression of high levels of Eomes is not constitutive but rather requires a continuum of cell-intrinsic NFκB signaling. Failure to maintain NFκB signals after the peak of the response led to impaired Eomes expression and a defect in the maintenance of CD8 T-cell memory. Strikingly, we found that antigen receptor [T-cell receptor (TCR)] signaling regulates this process through expression of the NFκB-dependent kinase proviral integration site for Moloney murine leukemia virus-1 (PIM-1), which in turn regulates NFκB and Eomes. T cells defective in TCR-dependent NFκB signaling were impaired in late expression of Pim-1, Eomes, and CD8 memory. These defects were rescued when TCR-dependent NFκB signaling was restored. We also found that NFκB–Pim-1 signals were required at memory to maintain memory CD8 T-cell longevity, effector function, and Eomes expression. Hence, an NFκB–Pim-1–Eomes axis regulates Eomes levels to maintain memory fitness.
Evaluation of an Intervention With Nurses for Delirium Detection After Cardiac Surgery

BACKGROUND Completion of a delirium detection tool allows rapid management, which alleviates complications. However, these tools are often underused. AIMS To assess the effect of a knowledge transfer (KT) intervention on the completion of a delirium detection tool by nurses working with cardiac surgery patients. Secondary aims included describing completion rates per work shift, and patient characteristics associated with higher rates. METHODS In a pre-post study, the intervention included a survey and focus groups to identify barriers to use of a delirium detection tool (Intensive Care Delirium Screening Checklist [ICDSC]). Nurses' suggestions for a KT activity and its implementation were also included. Using chi-square analysis and medical charts from 242 patients, we compared the pre- and postintervention rates of completion of the ICDSC. RESULTS The majority of nurses who completed the survey (n = 30) felt they had the knowledge, skills, and intention to complete the ICDSC. During the focus groups (n = 4), a need for information on delirium symptoms and its management was raised as a barrier. This barrier was addressed with the selected KT activity (clinical capsule and aide-memoire handed out to nurses [n = 24]). Across all work shifts, the completion rate was similar pre- and postintervention. Overall, the completion rate was lower during the day shift than the night and evening shifts. A higher rate was associated with the first three postoperative days, and longer hospital and intensive care unit stays. LINKING EVIDENCE TO ACTION A tailored intervention based on preidentified barriers and facilitators, using the Determinants of Implementation Behavior Questionnaire, and in collaboration with participants, has the potential to promote evidence-based practice.
Complex Document Classification and Localization Application on Identity Document Images

This paper studies the problem of document image classification. More specifically, we address the classification of documents composed of few textual information and complex background (such as identity documents). Unlike most existing systems, the proposed approach simultaneously locates the document and recognizes its class. The latter is defined by the document nature (passport, ID, etc.), emission country, version, and the visible side (main or back). This task is very challenging due to unconstrained capturing conditions, sparse textual information, and varying components that are irrelevant to the classification, e.g. photo, names, address, etc. First, a base of document models is created from reference images. We show that training images are not necessary and only one reference image is enough to create a document model. Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. The matching process is optimized to guarantee an execution time independent from the number of document models. Once the document model is found, a more accurate matching is performed to locate the document and facilitate information extraction. Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%.