Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional rod photoreceptor ablation reveals Sall1 as a microglial marker and regulator of microglial morphology in the retina

Neurodegeneration has been shown to induce microglial activation and the infiltration of monocyte‐derived macrophages into the CNS, resulting in the coexistence of these two populations within the same lesion, though their distinct features remain elusive. To investigate the impact of rod photoreceptor degeneration on microglial activation, we generated a toxin‐mediated genetic model of rod degeneration. Rod injury induced microglial proliferation and migration toward the photoreceptors. Bone marrow transplantation revealed the invasion of monocyte‐derived macrophages into the retina, with microglia and the infiltrating macrophages showing distinct distribution patterns in the retina. By comparing the gene expression profiles of the activated microglia and infiltrating macrophages, we identified microglia‐specific genes, including Ak1, Ctsf, Sall1, Phlda3, and Spns2. An analysis of Sall1gfp knock‐in mice showed GFP expression in the microglia of developing and mature healthy retinas. DTA injury induced the expansion of Sall1gfp+ microglia, whereas Ly6C+ monocyte‐derived macrophages were mostly Sall1gfp‐, supporting the idea that Sall1 is exclusively expressed in microglia within the retinal phagocyte pool. We evaluated the contribution of microglia to the phagocyte pool in rd1 mutant retinas and found that Sall1gfp+ microglia constituted the majority of phagocytes. A Sall1 deficiency did not affect microglial colonization of the retina and the cortex, but it did change their morphology from a ramified to a more amoeboid appearance. The morphological defects observed in Sall1‐deficient microglia were not rescued by the presence of wild‐type non‐microglial cells, suggesting that Sall1 functions cell‐autonomously in microglia. Taken together, our data indicate that Sall1 regulates microglial morphology during development. GLIA 2016;64:2005–2024
Closed-Loop Modulation of the Pathological Disorders of the Basal Ganglia Network

A generalized predictive closed-loop control strategy to improve the basal ganglia activity patterns in Parkinson’s disease (PD) is explored in this paper. Based on system identification, an input–output model is established to reveal the relationship between external stimulation and neuronal responses. The model contributes to the implementation of the generalized predictive control (GPC) algorithm that generates the optimal stimulation waveform to modulate the activities of neuronal nuclei. By analyzing the roles of two critical control parameters within the GPC law, optimal closed-loop control that has the capability of restoring the normal relay reliability of the thalamus with the least stimulation energy expenditure can be achieved. In comparison with open-loop deep brain stimulation and traditional static control schemes, the generalized predictive closed-loop control strategy can optimize the stimulation waveform without requiring any particular knowledge of the physiological properties of the system. This type of closed-loop control strategy generates an adaptive stimulation waveform with low energy expenditure with the potential to improve the treatments for PD.
Cardio-Protective Role of Gingerol along with Prominent Anti-Diabetic Cardiomyopathy Action in A Streptozotocin-Induced Diabetes Mellitus Rat Model

Objective Diabetic cardiomyopathy (DCM) is characterized as a coronary heart disease which expands during diabetes due to alterations in the myocardial function and structure. The currentstudy intends to elucidate the protective effect of gingerol on DCM in a streptozotocin (STZ)-induced diabetes mellitus (DM) rat model. Materials and Methods In this experimental study, the animals were divided into three groups: normal control, DM control, and DM+gingerol (10 mg/kg). The body weights of all rats were estimated at regular intervals. The myocardial profile, oxidative stress, and activities of metabolic enzymes were also scrutinized. The proinflammatory cytokine levels together with cellular protein expression connected with apoptosis were estimated via Western blot analysis. Results The rats that suffered from DCM exhibited abnormal levels of myocardial markers, aberrant metabolic enzymatic activity, elevated concentrations of inflammatory factors, and enhanced oxidative stress parameters along with increased cell death apoptosis. Whereas gingerol showed protective effects on the treated rats by an improved antioxidant defense system. Conclusion The current findings suggested that gingerol is effective in the treatment of DCM by inhibition of inflammation and oxidative stress.
Investigation Of Nanoparticle Delivery To Cancer Cells By Conjugation With Various Targeting Moieties

INVESTIGATION OF NANOPARTICLE DELIVERY TO CANCER CELLS BY CONJUGATION WITH VAROIUS TARGETING MOEITIES Leila Hossein Rashidi (M.S.) The University of Texas at Arlington, 2013 Supervising Professor: Wei Chen Recently, mapping and profiling of specific tumor biomarkers characterizing cancer cells, followed by understanding of signal cascades involved in the pathogenesis of tumors (targeted therapy), as well as diagnosis in early stages, have become promising topics in cancer research. Since, the most considerable limitations of conventional therapies and diagnostic modalities are associated with nonspecific targeting; designing intelligent new anti-cancer nanocarries improved by targeting biomolecules could enhance therapeutic efficiencies as well as diagnosis in early stage of cancer. Active targeting which is based on the surface accessibility and the high level of expression of specific cancer antigens has attracted much attention in the last decade, since it could result in highly efficient treatments and markedly reduced systemic toxicities. In this study two different nanoparticles (NPs) synthesized in this lab were used in order to conjugate with variety of biomolecules to investigate cancer cells targeting. Firstly, Sr3MgSi2O8:Eu, Dy persistent luminescence NPs were conjugated to folic acid (FA) through a simple EDC/NHS (1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide /Nhydroxysuccinimide) chemistry. Then, some characterization studies were conducted.
EFFECT OF FABRICATION PROCESS PARAMETERS ON THE SIZE OF GELATIN/NANOHYDROXYAPATITE MICROSPHERES

ano-hydroxyapatite/gelatin (nHA/Ge) microspheres are currently used in bone tissue engineering as bone filler. In this study, the effect of fabrication process parameters on the particle size of nano-hydroxyapatite/gelatin microspheres was investigated. The nHA/Ge microspheres were fabricated using water in oil emulsion. In order to design an experimental design, a surface response model with 2 factors including the rate of shaking and water to oil volume ratio in 3 levels was applied. Particle size was evaluated by using an optical microscope. The morphology of microspheres and distribution of nano-particles within the microspheres were studied by using scanning electron microscope and Ca elemental map obtained from energy dispersive X-ray analysis (EDX), respectively. Statistical analysis of the results obtained from particle size measurements revealed that the rate of shaking has stronger influence on the particle size of microspheres. Morphological studies showed that the fabricated microspheres were spherical with smooth surface. Ca elemental map of the microspheres showed that nano-hydroxyapatite particles distributed uniformly within the microspheres. microsphere, nanocomposite, gelatin/nanohydroxyapatite, particle size, surface response model
Effectiveness of Collaborative Care for Depression in Public-Sector Primary Care Clinics Serving Latinos.

OBJECTIVE Quality improvement interventions for depression care have been shown to be effective for improving quality of care and depression outcomes in settings with primarily insured patients. The aim of this study was to determine the impact of a collaborative care intervention for depression that was tailored for low-income Latino patients seen in public-sector clinics. METHODS A total of 400 depressed patients from three public-sector primary care clinics were enrolled in a randomized controlled trial of a tailored collaborative care intervention versus enhanced usual care. Social workers without previous mental health experience served as depression care specialists for the intervention patients (N=196). Depending on patient preference, they delivered a cognitive-behavioral therapy (CBT) intervention or facilitated antidepressant medication given by primary care providers or both. In enhanced usual care, patients (N=204) received a pamphlet about depression, a letter for their primary care provider stating that they had a positive depression screen, and a list of local mental health resources. Intent-to-treat analyses examined clinical and process-of-care outcomes at 16 weeks. RESULTS Compared with patients in the enhanced usual care group, patients in the intervention group had significantly improved depression, quality of life, and satisfaction outcomes (p<.001 for all). Intervention patients also had significantly improved quality-of-care indicators, including the proportion of patients receiving either psychotherapy or antidepressant medication (77% versus 21%, p<.001). CONCLUSIONS Collaborative care for depression can greatly improve care and outcomes in public-sector clinics. Social workers without prior mental health experience can effectively provide CBT and manage depression care.
The logistics of myelin biogenesis in the central nervous system

Rapid nerve conduction depends on myelin, but not all axons in the central nervous system (CNS) are myelinated to the same extent. Here, we review our current understanding of the biology of myelin biogenesis in the CNS. We focus on how the different steps of myelination are interconnected and how distinct patterns of myelin are generated. Possibly, a “basal” mode of myelination is laying the groundwork in areas devoted to basic homeostasis early in development, whereas a “targeted” mode generates myelin in regions controlling more complex tasks throughout adulthood. Such mechanisms may explain why myelination progresses in some areas according to a typical chronological and topographic sequence, while in other regions it is regulated by environmental stimuli contributing to interindividual variability of myelin structure. GLIA 2017;65:1021–1031
BASED ALGORITHM FOR OPTIMIZATION WITH EIGENVALUE

Optimization of convex functions subject to eigenvalue constraints is intriguing because of peculiar analytical properties of eigenvalue functions and is of practical interest because of a wide range of applications in fields such as structural design and control theory. Here we focus on the optimization of a linear objective subject to a constraint on the smallest eigenvalue of an analytic and Hermitian matrix-valued function. We propose a numerical approach based on quadratic support functions that overestimate the smallest eigenvalue function globally. The quadratic support functions are derived by employing variational properties of the smallest eigenvalue function over a set of Hermitian matrices. We establish the local convergence of the algorithm under mild assumptions and deduce a precise rate of convergence result by viewing the algorithm as a fixed point iteration. The convergence analysis reveals that the algorithm is immune to the nonsmooth nature of the smallest eigenvalue. We illustrate the practical applicability of the algorithm on the pseudospectral functions.
Closed-Loop Compressive CSIT Estimation in FDD Massive MIMO Systems With 1 Bit Feedback

One major practical issue for the implementation of frequency division duplex (FDD) massive MIMO systems is that the acquisition of channel state information at the transmitter side (CSIT) requires overwhelming pilot training and feedback overhead. Recently, compressive sensing (CS) based CSIT estimation approaches have been proposed to reduce the pilot training overhead for massive MIMO systems. However, it is very difficult to compute the minimum required pilot training overhead at the base station because of loose restricted isometry property (RIP) bounds for successful CS recovery and unknown sparsity levels. In this paper, we consider a framework of closed-loop compressive CSIT estimation with 1 bit feedback to learn the minimum required pilot overhead to achieve a certain target CSIT MSE without explicit knowledge of channel sparsity. We analyze the convergence behaviors of the multi-loop pilot overhead adaptation based on the Lyapunov approach. Simulations show that the proposed closed-loop compressive CSIT estimation framework has substantial performance gain over conventional open-loop algorithms and is very robust to dynamic sparsity as well as model mismatch.
Visual Deficit From Laser Interstitial Thermal Therapy for Temporal Lobe Epilepsy: Anatomical Considerations.

BACKGROUND Laser interstitial thermal therapy (LITT) is quickly emerging as an effective surgical therapy for temporal lobe epilepsy (TLE). One of the most frequent complications of the procedure is postoperative visual field cuts, but the physiopathology of these deficits is unknown. OBJECTIVE To evaluate potential causes of visual deficits after LITT for TLE in an attempt to minimize this complication. METHODS This retrospective chart review compares the case of a 24-year-old male who developed homonymous hemianopsia following LITT for TLE to 17 prior patients who underwent the procedure and suffered no visual deficit. We examined both features of the surgical approach (trajectory, laser energy, ablation size) and of preoperative surgical anatomy, derived from volumetric tracings of mesiotemporal structures. RESULTS For the patient with postoperative homonymous hemianopsia imaging suggested inadvertent ablation of the lateral geniculate nucleus, although the laser was positioned entirely within the hippocampus. This patient's laser trajectory, ablation number, energy delivered, and ablation size were not significantly different from the prior patients. However, the subject with the visual deficit did have significantly smaller choroidal fissure cerebrospinal fluid volume. CONCLUSION Visual deficits are the most common complication of LITT for mesiotemporal epilepsy and patients at most risk may have small cerebrospinal fluid volume in the choroidal fissure, allowing heat to spread from the hippocampal body to the lateral geniculate nucleus. When such anatomy is identified on preoperative magnetic resonance imaging, we recommend lowering laser trajectory, decreasing ablation power through the hippocampal body, and using temperature safety markers at the lower thalamic border.
Biosensor and its Applications

The paper presents an absolutely unique design approach for earthquake prediction with exact timing detail, using electrical behavior of bio-molecules and nanotechnology. Many attempts have been made till now to predict seismic events, but making a successful earthquake forecast is still a challenge before the scientific community. The studies carried out in the past using traditional seismological methods have solved the problem of long term prediction to a much extent [1]. However, the problem of short term prediction remains yet unsolved.
Fuzzy Classifiers for Imbalanced , Complex Classes of Varying Size

In this paper we investigate the suitability of a fuzzy system as a classifier for imbalanced data problems. Primarily, the fuzzy model performance is evaluated on artificial data sets, generated with various levels of size, complexity and imbalance. It is investigated what combination of the three problematic issues makes the learning problem harder [4]. A theoretic analysis shows that for a fuzzy classifier the “imbalance problem” is no longer a problem. By considering a relative frequency to the class sizethe imbalance factor is eliminated.
Diverse C-Terminal Sequences Involved in Flavobacterium johnsoniae Protein Secretion

ABSTRACT Flavobacterium johnsoniae and many related bacteria secrete proteins across the outer membrane using the type IX secretion system (T9SS). Proteins secreted by T9SSs have amino-terminal signal peptides for export across the cytoplasmic membrane by the Sec system and carboxy-terminal domains (CTDs) targeting them for secretion across the outer membrane by the T9SS. Most but not all T9SS CTDs belong to the family TIGR04183 (type A CTDs). We functionally characterized diverse CTDs for secretion by the F. johnsoniae T9SS. Attachment of the CTDs from F. johnsoniae RemA, AmyB, and ChiA to the foreign superfolder green fluorescent protein (sfGFP) that had a signal peptide at the amino terminus resulted in secretion across the outer membrane. In each case, approximately 80 to 100 amino acids from the extreme carboxy termini were needed for efficient secretion. Several type A CTDs from distantly related members of the phylum Bacteroidetes functioned in F. johnsoniae, supporting the secretion of sfGFP by the F. johnsoniae T9SS. F. johnsoniae SprB requires the T9SS for secretion but lacks a type A CTD. It has a conserved C-terminal domain belonging to the family TIGR04131, which we refer to as a type B CTD. The CTD of SprB was required for its secretion, but attachment of C-terminal regions of SprB of up to 1,182 amino acids to sfGFP failed to result in secretion. Additional features outside the C-terminal region of SprB may be required for its secretion. IMPORTANCE Type IX protein secretion systems (T9SSs) are common in but limited to members of the phylum Bacteroidetes. Most proteins that are secreted by T9SSs have conserved carboxy-terminal domains that belong to the protein domain family TIGR04183 (type A CTDs) or TIGR04131 (type B CTDs). Here, we identify features of T9SS CTDs of F. johnsoniae that are required for protein secretion and demonstrate that type A CTDs from distantly related members of the phylum function with the F. johnsoniae T9SS to secrete the foreign protein sfGFP. In contrast, type B CTDs failed to target sfGFP for secretion, suggesting a more complex association with the T9SS.
Traceability for Adaptive Information Security in the Cloud

One of the key challenges in cloud computing is the security of the consumer data stored and processed by cloud machines. When the usage context of a cloud application changes, or when the context is unknown, there is a risk that security policies are violated. To minimize this risk, cloud applications need to be engineered to adapt their security policies to maintain satisfaction of security requirements despite changes in their usage context. We call such adaptation capability Adaptive Information Security. The paper argues that one of the prerequisites to adaptive information security is the use of traceability as a means to understanding the relationship between security requirements and security policies. Using an example, we motivate the need for improving traceability in the development of cloud applications.
Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy

Motivation: Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub‐cellular structures, and their local spatial context. Results: The proposed method was effective at improving spectral unmixing performance by exploiting: (i) a set of dictionary‐based models for object morphologies learned from the image data; and (ii) models of spatial context learned from the image data using a total variation algorithm. The method was evaluated on multi‐spectral images of multiplex‐labeled pancreatic ductal adenocarcinoma (PDAC) tissue samples. The former constraint ensures that neighbouring pixels correspond to morphologically similar structures, and the latter constraint ensures that neighbouring pixels have similar spectral signatures. The average Mean Squared Error (MSE) and Signal Reconstruction Error (SRE) ratio of the proposed method was 39.6% less and 8% more, respectively, compared to the best of all other algorithms that do not exploit these spatial constraints. Availability and Implementation: Open source software (MATLAB). Contact: broysam@central.uh. edu Supplementary information: Supplementary data are available at Bioinformatics online.
A Clinical Evidence-Based Approach to Examine the Effects of mTBI and PTSD Symptoms on ANAM Performance in Recently Deployed Active Duty Soldiers: Results From the Warrior Strong Study

Objective: Service members are frequently diagnosed with comorbid mild traumatic brain injury (mTBI) and posttraumatic stress disorder after returning from Afghanistan and Iraq. Little is known about how mTBI in the postacute and chronic phases combined with current posttraumatic stress disorder symptoms (PTS) affects performance on the Automated Neuropsychological Assessment Metrics, Version 4, Traumatic Brain Injury—Military (ANAM4) battery used by the US military. We examined postdeployment ANAM4 performance using conventional statistical methods, as well as rates of poor performance, below established cutoffs (<10th and ⩽2nd percentile). Methods: A total of 868 soldiers were assessed for history of mTBI during the most recent deployment, as well as, lifetime mTBI, current PTS, and current pain-related symptoms. The ANAM4 was also administered. Results: Soldiers with PTS and/or mTBI performed worse on ANAM4 relative to controls with those with both conditions performing worst. However, a nontrivial minority (∼10%-30%) of individuals with mTBI, PTS, or both had scores that were at or below the second percentile. Conclusion: Our results illustrate that a combination of mTBI and PTS is associated with worse ANAM4 performance than either condition alone. Furthermore, only a minority of soldiers with any, or both, of the conditions had ANAM deficits. The long-lasting impacts and unique contribution of each condition have yet to be determined.
Machine learning-based IDS for software-defined 5G network.

: As an inevitable trend of future fifth generation (5G) networks, software-defined architecture has many advantages in providing centralised control and flexible resource management. However, it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, intrusion detection systems (IDSs) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software-defined 5G. In this study, the authors propose an intelligent IDS taking the advances in software-defined technology and artificial intelligence based on software-defined 5G architecture. It flexibly integrates security function modules which are adaptively invoked under centralised management and control with a global view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent IDS achieves better performance with lower overhead. It is also verified that the selected machine learning algorithms show better accuracy and reduced false alarm rate in flow-based classification.