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autoimmune hepatitis (DAIH) is an important cause of late allograft dysfunction following liver transplantation, but its cause and underlying pathogenesis remains unclear. We sought to identify specific innate and adaptive immune mechanisms driving the pro-inflammatory cytokine secreting regulatory T cell (Treg) phenotype in DAIH and determine if modulation of these pathways could resolve the inflammatory milieu observed in the livers of patients with DAIH. Here, we demonstrate toll-like receptors (TLRs) 2- and 4-mediated inflammasome activation in CD14 monocytes, a finding that is key to maintaining dysfunctional Tregs in patients with DAIH. Furthermore, silencing of TLR 2 and 4 in CD14 monocytes prevented activation of the inflammasome and significantly decreased IFN-γ production by FOXP3 Tregs. We also observed significantly increase in expression of tumor necrosis factor α-induced protein 3 ( ), a negative regulator of the NLRP3 Inflammasome, in monocytes/macrophages of liver transplant subjects who have normal allograft function and do not have DAIH. expression was virtually absent in monocytes/macrophages of patients with DAIH. Our findings suggest that autoimmunity in DAIH is promoted by CD14 monocytes predominantly through activation of inflammatory signaling pathways.

A crucial issue of recent years is whether a host state is able to bring a counterclaim against a foreign investor in an investor-state arbitration proceeding. Several Latin American states have attempted to make foreign corporations accountable for environmental damages and human rights violations in front of international tribunals. This essay focuses on investment arbitrations governed by the Convention on the Settlement of Investment Disputes between States and Nationals of Other States (\"ICSID Convention\") and examines two questions raised by the landmark case Urbaser v. Argentina. First, how does a tribunal decide whether parties have consented to arbitrate counterclaims? Second, when is a counterclaim closely related to an investor claim? Consent and close connection are two requirements for a tribunal to decide a counterclaim under the ICSID Convention. Urbaser made two significant shifts in counterclaim jurisprudence. First, before Urbaser, International Centre for Settlement of Investment Disputes (\"ICSID\" or \"Centre\") tribunals tended to be careful in extending jurisdiction to counterclaims and declined to do so when the language of a bilateral investment treaty (\"BIT\") does not expressly provide parties' consent to arbitrate counterclaims. As BITs are primarily designed to protect investors' interests when entering a foreign country, most of the treaties are silent on counterclaims. Under the Urbaser approach, however, parties would be found to have implicitly consented to arbitrate counterclaims if a treaty uses neutral language that permits either an investor or a state to submit a dispute to an arbitration tribunal. Besides, even if a treaty does not expressly impose obligations upon investors, investors may be subject to obligations from external sources of international law as the treaty refers to general principles of international law. This approach lifts the burden of proof for a host state to demonstrate parties' consent to arbitrate counterclaims, but tribunals should strictly follow the text of a treaty and decline jurisdiction to a counterclaim if the language of the treaty strongly suggests otherwise. Aven v. Costa Rica, a recent decision, is an example where the tribunal wrongly found parties' consent without textual support. Second, also pre-Urbaser, tribunals distinguished counterclaims based on a provision of domestic law from those based on a provision of international law and found that the former type of counterclaims would not be closely connected to an investor's claims. This legal connection interpretation is inconsistent with the jurisprudence on tribunals jurisdiction over contract-based claims. On the other hand, the Urbaser tribunal significantly eased the nexus requirement and held that a factual link is sufficient. To promote the administration of justice in investor-state arbitration, this is the approach that tribunals should adopt in the future. Moving forward, the drafters of BITs should craft express provisions on substantive and procedural rules of counterclaims. That will reduce uncertainties in treaty interpretations while helping host states regulate both environmental damages and human rights violations arising out of the underlying investments. Arbitration institutions should also revise their rules on adjudicating counterclaims and address systematic flaws to promote consistency of counterclaim rulings. Uniformity in counterclaim jurisprudence is essential to establish the legitimacy of the investor-state dispute settlement (\"ISDS\").

Access to trustworthy artificial intelligence (AI) for clinical applications is uneven, especially in low-resource settings with limited and inconsistent data. Models from high-resource settings often fail to generalize. Transfer learning (TL) can adapt established models to new settings. Using neurological outcome prediction for out-of-hospital cardiac arrest (OHCA) as a proof of concept, we adapted a model trained on a large cohort to Vietnam (243 patients) and Singapore (15,916 patients) using the Pan-Asian Resuscitation Outcomes Study registry. The external model performed poorly on the Vietnam cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.467 (95% CI: 0.141–0.785), but TL markedly improved performance (AUROC = 0.807, 95% CI: 0.626–0.948). In Singapore, TL yielded modest gains (AUROC = 0.955 vs. 0.945). These findings highlights the potential of TL to improve prediction accuracy across diverse healthcare contexts and to support equitable and safe global AI adoption.

To characterize patients with coronavirus disease 2019 (covid-19) in a large New York City medical center and describe their clinical course across the emergency department, hospital wards, and intensive care units. Retrospective manual medical record review. NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical center in New York City. The first 1000 consecutive patients with a positive result on the reverse transcriptase polymerase chain reaction assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who presented to the emergency department or were admitted to hospital between 1 March and 5 April 2020. Patient data were manually abstracted from electronic medical records. Characterization of patients, including demographics, presenting symptoms, comorbidities on presentation, hospital course, time to intubation, complications, mortality, and disposition. Of the first 1000 patients, 150 presented to the emergency department, 614 were admitted to hospital (not intensive care units), and 236 were admitted or transferred to intensive care units. The most common presenting symptoms were cough (732/1000), fever (728/1000), and dyspnea (631/1000). Patients in hospital, particularly those treated in intensive care units, often had baseline comorbidities including hypertension, diabetes, and obesity. Patients admitted to intensive care units were older, predominantly male (158/236, 66.9%), and had long lengths of stay (median 23 days, interquartile range 12-32 days); 78.0% (184/236) developed acute kidney injury and 35.2% (83/236) needed dialysis. Only 4.4% (6/136) of patients who required mechanical ventilation were first intubated more than 14 days after symptom onset. Time to intubation from symptom onset had a bimodal distribution, with modes at three to four days, and at nine days. As of 30 April, 90 patients remained in hospital and 211 had died in hospital. Patients admitted to hospital with covid-19 at this medical center faced major morbidity and mortality, with high rates of acute kidney injury and inpatient dialysis, prolonged intubations, and a bimodal distribution of time to intubation from symptom onset.

BackgroundSorafenib is one of the standard first-line therapies for advanced hepatocellular carcinoma (HCC). Unfortunately, there are currently no appropriate biomarkers to predict the clinical efficacy of sorafenib in HCC patients. MicroRNAs (miRNAs) have been studied for their biological functions and clinical applications in human cancers.MethodsIn this study, we found that miR-10b-3p expression was suppressed in sorafenib-resistant HCC cell lines through miRNA microarray analysis.ResultsSorafenib-induced apoptosis in HCC cells was significantly enhanced by miR-10b-3p overexpression and partially abrogated by miR-10b-3p depletion. Among 45 patients who received sorafenib for advanced HCC, those with high miR-10b-3p levels, compared to those with low levels, exhibited significantly longer overall survival (OS) (median, 13.9 vs. 3.5 months, p = 0.021), suggesting that high serum miR-10b-3p level in patients treated with sorafenib for advanced HCC serves as a biomarker for predicting sorafenib efficacy. Furthermore, we confirmed that cyclin E1, a known promoter of sorafenib resistance reported by our previous study, is the downstream target for miR-10b-3p in HCC cells.ConclusionsThis study not only identified the molecular target for miR-10b-3p, but also provided evidence that circulating miR-10b-3p may be used as a biomarker for predicting sorafenib sensitivity in patients with HCC.

Deep learning and Convolutional Neural Network (CNN) have becoming increasingly more popular and important in both academic and industrial areas in recent years cause they are able to provide better accuracy and result in classification, detection and recognition areas, compared to traditional approaches. Currently, there are many popular frameworks in the market for deep learning development, such as Caffe, TensorFlow, Pytorch, and most of frameworks natively support CPU and consider GPU as the mainline accelerator by default. FPGA device, viewed as a potential heterogeneous platform, still cannot provide a comprehensive support for CNN development in popular frameworks, in particular to the training phase. In this paper, we firstly propose the FeCaffe, i.e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe to enable FPGA to support mainline deep learning development features, e.g. training and inference with Caffe. Furthermore, we provide some benchmarks with FeCaffe by taking some classical CNN networks as examples, and further analysis of kernel execution time in details accordingly. Finally, some optimization directions including FPGA kernel design, system pipeline, network architecture, user case application and heterogeneous platform levels, have been proposed gradually to improve FeCaffe performance and efficiency. The result demonstrates the proposed FeCaffe is capable of supporting almost full features during CNN network training and inference respectively with high degree of design flexibility, expansibility and reusability for deep learning development. Compared to prior studies, our architecture can support more network and training settings, and current configuration can achieve 6.4x and 8.4x average execution time improvement for forward and backward respectively for LeNet.

As Field-Programmable Gate Array (FPGA) capacity can now support several processors on a single device, the scalability of FPGA design tools and methods has emerged as a major obstacle for the wider use of FPGAs. For example, logic synthesis, which has traditionally been the fastest step in the FPGA Computer-Aided Design (CAD) flow, now takes several hours to complete in a typical FPGA compile. In this work, we address this problem by focusing on two areas. First, we revisit FPGA logic synthesis and attempt to improve its scalability. Specifically, we look at a binary decision diagram (BDD) based logic synthesis flow, referred to as FBDD, where we improve its runtime by several fold with a marginal impact to the resulting circuit area. We do so by speeding up the classical cut generation problem by an order-of-magnitude which enables its application directly at the logic synthesis level. Following this, we introduce a guided partitioning technique using a fast global budgeting formulation, which enables us to optimize individual “pockets” within the circuit without degrading the overall circuit performance. By using partitioning we can significantly reduce the solution space of the logic synthesis problem and, furthermore, open up the possibility of parallelizing the logic synthesis step. The second area we look at is the area of Engineering Change Orders (ECOs). ECOs are incremental modifications to a design late in the design flow. This is beneficial since it is minimally disruptive to the existing circuit which preserves much of the engineering effort invested previously in the design. In a design flow where most of the steps are fully automated, ECOs still remain largely a manual process. This can often tie up a designer for weeks leading to missed project deadlines which is very detrimental to products whose life-cycle can span only a few months. As a solution to this, we show how we can leverage existing logic synthesis techniques to automatically modify a circuit in a minimally disruptive manner. This can significantly reduce the turn-around time when applying ECOs.

This dissertation considers the following question: \"Should the sub-carriers in a multi-carrier system be spread or non-spread?\" We approach this problem by comparing the theoretical bit error rates of multi-carrier direct sequence code division multiple access (MC-DS-CDMA) and multi-carrier code division multiple access (MC-CDMA) for an asynchronous uplink. To ensure a fair comparison, we constrain both schemes to the same bandwidth, information rate, and energy-per-bit. Since MC-DS-CDMA uses direct sequence spreading at each sub-carrier, while each MC-CDMA sub-carrier is unspread, MC-CDMA employs a larger number of sub-carriers than MC-DS-CDMA over a given bandwidth. As a result, there potentially exists between the two schemes a trade-off between diversity gain and channel estimation errors. Initially, we compare MC-DS-CDMA and MC-CDMA under a single-input single-output (SISO) system and for two different channel scenarios (i.e., two different cases for the coherence bandwidth of the channel), but we then extend the comparison to a multiple-input multiple-output (MIMO) system employing dual transmit/receive diversity and space-time block coding. For both cases, we derive closed-form expressions of the bit error rates for both multi-carrier schemes, and we compare the numerical results for different information rates, number of users, and number of pilot symbols per channel estimate to determine the performance trade-offs that may exist between the two schemes. We also compare the MIMO results against those of the SISO system to determine the impact of additional diversity on these trade-offs.

Modern biometrics delivers an enhanced level of security by means of a \"proof of property\". The design and deployment of a biometric system, however, hide many pitfalls, which, when underestimated, can lead to major security weaknesses and privacy threats. Issues of concern include biometric identity theft and privacy invasion because of the strong connection between a user and his identity. This book showcases a collection of comprehensive references on the advances of biometric security t.