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Drug for treating tinnitus
No full textCompounds, pharmaceutical compositions, and methods for treating tinnitus in a subject in need thereof. The methods include administering a therapeutically effective amount of a compound having a structure represented by Formula I as described herein. The compounds and compositions are administered transdermally or orally, preferably via a sustained release mechanism. The compounds and compositions reduce at least one behavioral correlate of tinnitus and/or at least one neurophysiological correlate of tinnitus. The compounds and compositions reduce hyperactivity in the auditory system
Large area sintering test platform and associated method of use
No full textA large area sintering platform, system, and methodology. The system includes a convection oven with a projection window disposed within a top surface of the oven. A platform is disposed within the oven below the window at a spaced distance away from the window. A powder is positioned on top of the platform, with a thermocouple positioned within the powder on the platform. A high intensity projector moves in sync with the platform, and uses low intensities and long exposure times to project an image through the window onto the powder and sinter the powder to fabricate the desired model layer by layer
Methods and compositions related to modified green fluorescent protein (GFP) with an embedded foreign peptide
No full textDisclosed herein are methods and compositions comprising chimeric green fluorescent protein (GFP) molecules embedded with foreign molecules. Also disclosed are methods of treating a subject with chimeric GFP molecules embedded with foreign molecules. Further disclosed are methods of delivering a desired molecule to a cell, wherein said desired molecule is embedded within a chimeric GFP molecule
Batched query processing and optimization
No full textDisclosed are various embodiments for batched query processing and optimization in database management systems. A single algebraic expression is generated based at least in part on applying equivalence rules to algebraic expressions for a plurality of database queries of a database comprising a set of relations. The equivalence rules involve relational operators comprising Psi (Ï) operators. The database can be queried using a single database query to create a result that is equivalent to the plurality of database queries
Physiological modeling of multiphase intra-arterial CT angiography for hepatic embolization therapy
No full textSystems and methods are provided herein for determining areas of low blood flow, low blood perfusion, suboptimal treatment perfusion, and/or likelihood of tumor recurrence. For example patient-specific methods are provided for identifying areas of low perfusion in an anatomy of interest (such as a vascular region). Angiography data corresponding to the anatomy of interest can be used to generate a modified three-dimensional (3D) model of the anatomy of interest, which represents a fluid flow system exhibiting variant flow patterns. The model can take into account patient-specific data to simulate physical characteristics of the fluid flow system. This provides for accurate determination of localized areas of low perfusion, which can be indicative of potential tumor recurrence
Boreal Chickadee Notes from Alaska
No full textI banded 36 Boreal Chickadees (Poecile hudsonicus) and had 27 recaptures for a total of 63 encounters in Anchorage, Alaska, from 1990-1995. Boreal Chickadees mean weight and fat accumulation did not vary between seasons; overall annual mean weight was 11.4 g. During fall and winter measured weight increased during daylight hours, but in summer birds maintained a steady weight throughout the day. Winter weight gain throughout the day was calculated to equal about 11.7% of base body weight. Wing, tail, and tarsal measurement averages and ranges are provided. Body, crown, and remige molt was heaviest in late July and August, but light body molt continued into October. This note contributes to a scarcity of avian daily weight patterns in locations with extreme seasonal changes in photoperiod
Utilization of Bixin-Loaded Polycaprolactone Nanoparticles to Ameliorate E-Cigarette-Induced Damage to Human Bronchial Epithelial Cells
No full textElectronic cigarettes (e-cigs) have increased in popularity and usage over the last few decades. E-cigs are generally composed of a liquid containing nicotine flavoring chemicals, a battery, a vaporization chamber, and a coil that heats the liquid upon inhalation of the mouthpiece. E-cigs were initially introduced as a healthy alternative to cigarette smoking. However, recent research has demonstrated that e-cigs elicit comparable cytotoxic and dangerous effects to conventional cigarettes. Bixin has been introduced in recent research as a candidate for therapeutic applications due to its innate anticancer, antioxidative, and anti-inflammatory characteristics. Nanoparticles (NPs) have emerged over the past few decades as a powerful tool for therapeutic drug delivery, offering a potential method for treating various conditions and diseases. Bixin NPs (BXNPs) show promise as a viable method for treating e-cig-induced damage due to the inherent properties of bixin and the advantages of using NPs over conventional medicinal interventions. This study conducted MTT assays to analyze how cell mitochondrial activity responds to JUUL exposure and how subsequent bixin NP treatment can mitigate this damage. The data demonstrate that differing nicotine JUUL concentrations and flavors induce varying responses in cells. While the BXNPs did not elicit a therapeutic response, the cell reaction to BXNP treatment varied according to the nicotine concentration of JUULs. This study lays the groundwork for future experimental endeavors to investigate how the dose-dependent kinetics of BXNPs can more effectively attenuate JUUL–induced mitochondrial stress and how chronic exposure to JUULs impacts cell responses over time
Beyond the Hype: The Fundamental Challenges of Machine Learning-Based Android Malware Detection in Cybersecurity
No full textMachine learning (ML) algorithms have achieved remarkable success across various domains, including cybersecurity. Inspired by these advancements, the academic security community has explored numerous ML-based approaches for Android malware detection. While ML holds significant promise in this domain, its practical deployment faces substantial challenges, including data collection, feature selection, app representation across different models, performance instability across datasets, and inherent limitations of learning-based malware detection. These challenges can lead to overly optimistic detection results and weaken the reliability of malware detection frameworks.
Android malware detection has been extensively studied using both traditional ML and deep learning (DL) approaches. Although many state-of-the-art detection models, particularly those based on DL, claim superior performance, they are often evaluated on a limited scale without comprehensive benchmarking against traditional ML models across diverse datasets. This raises concerns about the robustness of DL-based approaches and the potential oversight of simpler, more efficient ML models. In this study, we conduct a systematic evaluation of Android malware detection models across four datasets: three publicly available, recently published datasets and a large-scale dataset we systematically collected. We implement a range of traditional ML and advanced DL models, revealing that while DL models can achieve strong performance, they are often compared against an insufficient number of traditional ML baselines. In many cases, simpler and more computationally efficient ML models yield comparable or even superior results, underscoring the need for rigorous benchmarking in Android malware detection research.
A critical aspect of ML-based malware detection is the numerical representation of apps for training and testing. We identify a widespread occurrence of distinct Android apps that have identical or nearly identical app representations. In particular, a significant portion of test samples may closely resemble or match representations of apps in the training dataset, leading to data leakage. This issue inflates the reported performance of ML models on the test set, creating an illusion of generalizability. Beyond overly optimistic assessments, data leakage can also result in qualitatively different research conclusions. We present two case studies to illustrate this impact and further examine the real-world implications using a leak-aware detection framework. Our findings demonstrate how the qualitatively different research conclusions can lead to incorrect recommendations regarding the most suitable ML models for practical deployment