The Future of Generic Biologics: Should the United States “Follow-On” the European Pathway?

The United States is embarking on a biotechnology drug revolution. In the last few decades, biotech drugs have saved millions of lives, and the market for these miracle cures continues to grow at an astronomical rate. Unfortunately, as the market for biotech drugs is skyrocketing, drug prices are following suit. As Congress strives to make these new drugs more affordable, it must not ignore significant safety concerns unique to these revolutionary therapies. Congress should follow the lead of the European Union to create an accessible pathway for generic forms of biotech drugs that includes strict regulatory measures to ensure drug safety and efficacy

Characterization of Bubble Transport in Porous Media Using a Microfluidic Channel

This study investigates the effect on varying flow rates and bubble sizes on gas–liquid flow through porous media in a horizontal microchannel. A simple bubble generation system was set up to generate bubbles with controllable sizes and frequencies, which directly flowed into microfluidic channels packed with different sizes of glass beads. Bubble flow was visualized using a high-speed camera and analyzed to obtain the change in liquid holdup. Pressure data were measured for estimation of hydraulic conductivity. The bubble displacement pattern in the porous media was viscous fingering based on capillary numbers and visual observation. Larger bubbles resulted in lower normalized frequency of the bubble breakthrough by 20 to 60 percent. Increasing the flow rate increased the change in apparent liquid holdup during bubble breakthrough. Larger bubbles and lower flow rate reduced the relative permeability of each channel by 50 to 57 percent and 30 to 64 percent, respectively

An Ensemble Multilabel Classification for Disease Risk Prediction

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance

On the Resistance of Prime-variable Rotation Symmetric Boolean Functions against Fast Algebraic Attacks

Boolean functions used in stream ciphers should have many cryptographic properties in order to help resist different kinds of cryptanalytic attacks. The resistance of Boolean functions against fast algebraic attacks is an important cryptographic property. Deciding the resistance of an n-variable Boolean function against fast algebraic attacks needs to determine the rank of a square matrix over finite field GF(2). In this paper, for rotation symmetric Boolean functions in prime n variables, exploiting the properties of partitioned matrices and circulant matrices, we show that the rank of such a matrix can be obtained by determining the rank of a reduced square matrix with smaller size over GF(2), so that the computational complexity decreases by a factor of n to the power omega for large n, where omega is approximately equal to 2.38 and is known as the exponent of the problem of computing the rank of matrices

Self-bilinear Map from One Way Encoding System and Indistinguishability Obfuscation

The bilinear map whose domain and target sets are identical is called the self-bilinear map. Original self-bilinear maps are defined over cyclic groups. This brings a lot of limitations to construct secure self-bilinear schemes. Since the map itself reveals information about the underlying cyclic group, hardness assumptions on DDHP and CDHP may not hold any more. In this paper, we used $i\mathcal{O}$ to construct a self-bilinear map from generic sets. These sets should own several properties. A new notion, One Way Encoding System (OWES), is proposed to describe formally the properties those sets should hold. An Encoding Division Problem is defined to complete the security proof. As an instance of the generic construction, we propose a concrete scheme built on the GGH graded encoding system and state that any $1$-graded encoding system may satisfy the requirements of OWES. Finally, we discuss the hardness of EDP in the GGH graded encoding system

On the (Fast) Algebraic Immunity of Boolean Power Functions

The (fast) algebraic immunity, including (standard) algebraic immunity and the resistance against fast algebraic attacks, has been considered as an important cryptographic property for Boolean functions used in stream ciphers. This paper is on the determination of the (fast) algebraic immunity of a special class of Boolean functions, called Boolean power functions. An n-variable Boolean power function f can be represented as a monomial trace function over finite field GF(2^n). To determine the (fast) algebraic immunity of Boolean power functions one may need the arithmetic in GF(2^n), which may be not computationally efficient compared with the operations over GF(2). We provide two sufficient conditions for Boolean power functions such that their immunities can determined only by the computations in GF(2). We show that Niho functions and a number of odd variables Kasami functions can satisfy the conditions

Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation

Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public