The clinical and EEG features and mutation analysis in a Chinese patient with severe hypoplasia of the cerebellum and pons

Abstract We report here a Chinese female infant with severe hypoplasia of the cerebellum and pons, and heterozygous mutation (c.18G >T, p.E6D) in the TSEN54 gene. This mutation was not present in her parents and the 100 Chinese controls, which proved to be a de novo missense mutation. MR imaging of the patient revealed severe hypoplasia of the bilateral cerebellar hemispheres and vermis with moderate flattening of the pons. A video EEG during hospitalization demostrated abnormal background activities and generalized burst and attenuation patterns during interictal stage. The spasms and tonic spasms occurred frequently in clusters with generalized voltage attenuation

Targeting fibroblast activation protein in tumor stroma with chimeric antigen receptor T cells can inhibit tumor growth and augment host immunity without severe toxicity.

The majority of chimeric antigen receptor (CAR) T-cell research has focused on attacking cancer cells. Here, we show that targeting the tumor-promoting, nontransformed stromal cells using CAR T cells may offer several advantages. We developed a retroviral CAR construct specific for the mouse fibroblast activation protein (FAP), comprising a single-chain Fv FAP [monoclonal antibody (mAb) 73.3] with the CD8α hinge and transmembrane regions, and the human CD3ζ and 4-1BB activation domains. The transduced muFAP-CAR mouse T cells secreted IFN-γ and killed FAP-expressing 3T3 target cells specifically. Adoptively transferred 73.3-FAP-CAR mouse T cells selectively reduced FAP(hi) stromal cells and inhibited the growth of multiple types of subcutaneously transplanted tumors in wild-type, but not FAP-null immune-competent syngeneic mice. The antitumor effects could be augmented by multiple injections of the CAR T cells, by using CAR T cells with a deficiency in diacylglycerol kinase, or by combination with a vaccine. A major mechanism of action of the muFAP-CAR T cells was the augmentation of the endogenous CD8(+) T-cell antitumor responses. Off-tumor toxicity in our models was minimal following muFAP-CAR T-cell therapy. In summary, inhibiting tumor growth by targeting tumor stroma with adoptively transferred CAR T cells directed to FAP can be safe and effective, suggesting that further clinical development of anti-human FAP-CAR is warranted

Cerebral Blood Volume Changes During Radiotherapy May Predict Pseudoprogression versus Disease Progression for Patients with High Grade Glioma

https://openworks.mdanderson.org/sumexp21/1040/thumbnail.jp

Optimal Blood Pressure Control Target for Older Patients with Hypertension: A Systematic Review and Meta-Analysis

Objective: This study evaluated the optimal systolic blood pressure (SBP) target for older patients with hypertension.Method: A Bayesian network meta-analysis was conducted. The risk of bias of the included studies was assessed by using a modified version of the Cochrane risk of bias. The trial outcomes comprised the following clinical events: major adverse cardiovascular events (MACE), cardiovascular mortality, all-cause mortality, myocardial infarction, heart failure and stroke.Results: A total of six trials were included. We reclassified all treatment therapies into three conditions according to the final achieved SBP after intervention (<130 mmHg, 130–139 mmHg and ≥140 mmHg). Our results demonstrated that anti-hypertensive treatment with an SBP target <130 mmHg, compared with treatment with an SBP target ≥140 mmHg, significantly decreased the incidence of MACE (OR 0.43, 95%CI 0.19–0.76), but no statistical difference was found in other comparisons. Although the results showed a trend toward more intensive anti-hypertension therapy having better effects on preventing cardiovascular mortality, all-cause mortality, myocardial infarction, heart failure, and stroke, no significant differences were found among groups.Conclusions: Our meta-analysis suggested that SBP <130 mmHg might be the optimal BP control target for patients ≥60 years of age; however, further evidence is required to support our findings

Bifunctional metal phosphide FeMnP films from single source metal organic chemical vapor deposition for efficient overall water splitting

Developing stable and efficient bifunctional catalysts for overall water splitting into hydrogen and oxygen is a critical step in the realization of several clean-energy technologies. Here we report a robust and highly active electrocatalyst that is constructed by deposition of the ternary metal phosphide FeMnP onto graphene-protected nickel foam by metal-organic chemical vapor deposition from a single source precursor. FeMnP exhibits high electrocatalytic activity toward both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Utilizing FeMnP/GNF as both the anode and the cathode for overall water splitting, a current density of 10 mA cm−2 is achieved at a cell voltage of as low as 1.55 V with excellent stability. Complementary density functional theory (DFT) calculations suggest that facets exposing both Fe and Mn sites are necessary to achieve high HER activity. The present work provides a facile strategy for fabricating highly efficient electrocatalysts from earth-abundant materials for overall water splitting

DependData: data collection dependability through three-layer decision-making in BSNs for healthcare monitoring

Recently, there have been extensive studies on applying security and privacy protocols in Body Sensor Networks (BSNs) for patient healthcare monitoring (BSN-Health). Though these protocols provide adequate security to data packets, the collected data may still be compromised at the time of acquisition and before aggregation/storage in the severely resource-constrained BSNs. This leads to data collection frameworks being meaningless or undependable, i.e., an undependable BSN-Health. We study data dependability concerns in the BSN-Health and propose a data dependability verification framework named DependData with the objective of verifying data dependability through the decision-making in three layers. The 1st decision-making (1-DM) layer verifies signal-level data at each health sensor of the BSN locally to guarantee that collected signals ready for processing and transmission are dependable so that undependable processing and transmission in the BSN can be avoided. The 2nd decision-making (2-DM) layer verifies data before aggregation at each local aggregator (like clusterhead) of the BSN to guarantee that data received for aggregation is dependable so that undependable data aggregation can be avoided. The 3rd decision-making (3-DM) layer verifies the stored data before the data appears to a remote healthcare data user to guarantee that data available to the owner end (such as smartphone) is dependable so that undependable information viewing can be avoided. Finally, we evaluate the performance of DependData through simulations regarding 1-DM, 2-DM, and 3-DM and show that up to 92% of data dependability concerns can be detected in the three layers. To the best of our knowledge, DependData would be the first framework to address data dependability aside from current substantial studies of security and privacy protocols. We believe the three layers decision-making framework would attract a wide range of applications in the future

Spin-Orbital Coupling in All-Inorganic Metal-Halide Perovskites: the Hidden Force that Matters

Highlighted with improved long-term thermal and environmental stability, all-inorganic metal halide perovskites exhibit tunable physical properties, cost-effective synthesis, and satisfactory optoelectronic performance, attracting increasing research interests worldwide. However, a less explored feature of these materials is their strong spin-orbit coupling (SOC), which is the hidden force influencing not only band structure but also properties including magnetoresistance, spin lifetime and singlet-triplet splitting. This review provides an overview of the fundamental aspects and the latest progress of the SOC and debate regarding Rashba effects in all-inorganic metal halide perovskites, providing critical insights into the physical phenomena and potential applications. Meanwhile, crystal structures and photophysics of all-inorganic perovskite are discussed in the context of SOC, along with the related experimental and characterization techniques. Furthermore, a recent understanding of the band topology in the all-inorganic halide perovskites is introduced to push the boundary even further for the novel applications of all-inorganic halide perovskites. Finally, an outlook is given on the potential directions of breakthroughs via leveraging the SOC in halide perovskites.Comment: 44 pages, 5 figure

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.Comment: 5 page

RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI

BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system. OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements. RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time. CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate