Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.

Stockhusen and Tantau (IPEC 2013) defined the operators paraW and paraBeta for parameterised space complexity classes by allowing bounded nondeterminism with multiple read and read-once access, respectively. Using these operators, they obtained characterisations for the complexity of many parameterisations of natural problems on graphs.

In this article, we study the counting versions of such operators and introduce variants based on tail-nondeterminism, paraW[1] and paraBetaTail, in the setting of parameterised logarithmic space. We examine closure properties of the new classes under the central reductions and arithmetic operations. We also identify a wide range of natural complete problems for our classes in the areas of walk counting in digraphs, first-order model-checking and graph-homomorphisms. In doing so, we also see that the closure of #paraBetaTail-L under parameterised logspace parsimonious reductions coincides with #paraBeta-L. We show that the complexity of a parameterised variant of the determinant function is #paraBetaTail-L-hard and can be written as the difference of two functions in #paraBetaTail-L for (0,1)-matrices. Finally, we characterise the new complexity classes in terms of branching programs.

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for discovering compromised accounts by semantic analysis of text messages coming out from an account. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We use our framework to develop specific algorithms that use the difference of language models of users and adversaries as features in a supervised learning setup. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem. This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an AP@0.5 of 78.67\% in our empirical evaluation.

Graph convolution network (GCN) have achieved state-of-the-art performance in the task of node prediction in the graph structure. However, with the gradual various of graph attack methods, there are lack of research on the robustness of GCN. At this paper, we will design a robust GCN method for node prediction tasks. Considering the graph structure contains two types of information: node information and connection information, and attackers usually modify the connection information to complete the interference with the prediction results of the node, we first proposed a method to hide the connection information in the generator, named Anonymized GCN (AN-GCN). By hiding the connection information in the graph structure in the generator through adversarial training, the accurate node prediction can be completed only by the node number rather than its specific position in the graph. Specifically, we first demonstrated the key to determine the embedding of a specific node: the row corresponding to the node of the eigenmatrix of the Laplace matrix, by target it as the output of the generator, we designed a method to hide the node number in the noise. Take the corresponding noise as input, we will obtain the connection structure of the node instead of directly obtaining. Then the encoder and decoder are spliced both in discriminator, so that after adversarial training, the generator and discriminator can cooperate to complete the encoding and decoding of the graph, then complete the node prediction. Finally, All node positions can generated by noise at the same time, that is to say, the generator will hides all the connection information of the graph structure. The evaluation shows that we only need to obtain the initial features and node numbers of the nodes to complete the node prediction, and the accuracy did not decrease, but increased by 0.0293.

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The saliency and separability of the hidden representations when trained on MNIST dataset is studied using an external classifier, and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.

Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization and performs much better on data in the wild.

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

Multi-view data capture permits free-viewpoint video (FVV) content creation. To this end, several users must capture video streams, calibrated in both time and pose, framing the same object/scene, from different viewpoints. New-generation network architectures (e.g. 5G) promise lower latency and larger bandwidth connections supported by powerful edge computing, properties that seem ideal for reliable FVV capture. We have explored this possibility, aiming to remove the need for bespoke synchronisation hardware when capturing a scene from multiple viewpoints, making it possible through off-the-shelf mobiles. We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures. We have designed an edge computing unit that supervises the relaying of timing triggers to and from multiple mobiles, in addition to synchronising frame harvesting. We empirically show the benefits of our edge computing unit by analysing latencies and show the quality of 3D reconstruction outputs against an alternative and popular centralised solution based on Unity3D.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

Over the years, performance evaluation has become essential in computer vision, enabling tangible progress in many sub-fields. While talking-head video generation has become an emerging research topic, existing evaluations on this topic present many limitations. For example, most approaches use human subjects (e.g., via Amazon MTurk) to evaluate their research claims directly. This subjective evaluation is cumbersome, unreproducible, and may impend the evolution of new research. In this work, we present a carefully-designed benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies. As for evaluation, we either propose new metrics or select the most appropriate ones to evaluate results in what we consider as desired properties for a good talking-head video, namely, identity preserving, lip synchronization, high video quality, and natural-spontaneous motion. By conducting a thoughtful analysis across several state-of-the-art talking-head generation approaches, we aim to uncover the merits and drawbacks of current methods and point out promising directions for future work. All the evaluation code is available at: https://github.com/lelechen63/talking-head-generation-survey.

Artificial intelligence technology has made its way absolutely necessary in a variety of industries including the fitness industry. Human pose estimation is one of the important researches in the field of Computer Vision for the last few years. In this project, pose estimation and deep machine learning techniques are combined to analyze the performance and report feedback on the repetitions of performed exercises in real-time. Involving machine learning technology in the fitness industry could help the judges to count repetitions of any exercise during Weightlifting or CrossFit competitions.

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time.

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

By Sci Dev Net Advances in printed solar cell technology promise clean renewable energy, opening possibilities for 1.3 billion people still without electric power in developing countries. The technology, which only requires the use of existing industrial-size printers, can produce … Continue reading →

[IMAGE-1]Welcome back to the jungle. And welcome to an unfortunate new Christmas movie tradition: the Jumanji movie.…

With all due respect to Greta Thunberg and Billie Eilish, nobody had a better 2019 than Baby Yoda. The real star of the Disney+ flagship Star Wars series The Mandalorian, the little green puppeteering/CGI marvel (aka "the Child") might be the most adorable creature ever created.…

By Michael Crowley The New York Times Company…

A catastrophic loss in biodiversity, reckless destruction of wildland and warming temperatures have allowed disease to explode. Ignoring the connection between climate change and pandemics would be “dangerous delusion,” one scientist said. The scientists who study how diseases emerge in a changing environment knew this moment was coming.…

Every serious pinball player remembers their first machine.…

New TV ads that started airing on morning shows throughout the Spokane region are aimed at grocery shoppers, but they're not hawking deals on cabbage or Cap'n Crunch.…

Disclosed is a method for preparing optically pure (+)-ambrisentan and (+)-darusentan, comprising: firstly catalyzing the asymmetric epoxidation of a β-unsaturated alkene using a chiral ketone derived from fructose or a hydrate thereof as a catalyst, and then subjecting the product to an epoxy compound ring-opening reaction and substitution reaction successively to obtain optically pure (+)-ambrisentan and (+)-darusentan.

Various embodiments of the present invention provide systems and methods for data processing. As an example, a data processing circuit is disclosed that includes a data detector circuit. The data detector circuit includes an anti-causal noise predictive filter circuit and a data detection circuit. In some cases, the anti-causal noise predictive filter circuit is operable to apply noise predictive filtering to a detector input to yield a filtered output, and the data detection circuit is operable to apply a data detection algorithm to the filtered output derived from the anti-causal noise predictive filter circuit.

A method of automating the deployment of a number of enterprise applications on one or more computer data processing systems. Each enterprise application or update is stored in a dynamic distribution directory and is provided with identifying indicia, such as stage information, target information, and settings information. When automated enterprise deployment is invoked, computer instructions in a computer readable medium provide for initializing deployment, performing deployment, and finalizing deployment of the enterprise applications or updates.

When a user enters text into an application, the application can utilize an auto-complete feature to provide the user with estimations as to a complete term a user is attempting to enter into the application. Visualization can be provided along with an estimation to disclose the likelihood the estimation is what the user intends to enter. Furthermore, a rationale can be provided to the user for the reason an estimation was provided to the user.

Alkyl nitrate ester compounds are provided for the delivery of nitric oxide to targeted muscle tissues, and in particular, to normal and dystrophic muscles. In one aspect, nitrate ester compounds are provided having the following formula: wherein, R1 is ONO2, CH2ONO2, CnH2n+1OH, CnH2n+1OH, or CH2CH2CH3, or H;R2 is ONO2, CH2ONO2, Cn'H2n'+1OH, Cn'H2n'+1OH, CH2CH2CH3 or H; andR3 is ONO2, CH2ONO2, Cn'″H2n″+1OH, Cn″H2n″+1OH, CH2CH2CH3 or H; wherein n is an integer from 0 to 9, n' is an integer from 0 to 9, and n″ is an integer from 0 to 9, and n+n'+n″≦9, and wherein at least one of R1, R2, and R3 is an ester nitrate selected from the group consisting of ONO2, CH2ONO2, and combinations thereof.

Provided herein are methods, compounds, and compositions for reducing expression of PTP1B mRNA and protein in an animal. Such methods, compounds, and compositions are useful to treat, prevent, delay, or ameliorate metabolic disease, for example, diabetes, or a symptom thereof.

A pharmaceutical composition comprising lectins is anti-tumorigenic and anti-viral, bacterial or protozoan. The composition, termed BiOmune is also useful for imaging, diagnosis and therapy of cancer.

An antisense molecule capable of binding to a selected target site to induce exon skipping in the dystrophin gene, as set forth in SEQ ID NO: 1 to 202.

A cytosine analog, a method of preparation of a cytosine analog, a DNA methyltransferase 1 inhibitor, and a method for DNA methylation inhibition, is provided for the treatment of diseases associated with deviations from normal DNA methylation. The analog of cytosine may be comprised of 1, N4, 5 and 6-substituted derivatives of cytosine or 5,6-dihydrocytosine, wherein the analog can be described by the chemical formula where R1 is H, R3, R4, 2'-deoxyribosyl, R4 is alkyl or aryl, X is N or C, wherein if X in the analog of formula I is N, then R5 is no substituent and if X in the analog of formula I and/or II is C or if X in the analog of formula II is N, then R5 and R6 are independently alkyl, aryl, hydroxyalkyl, aminoalkyl, hydroxyl, carboxyl, amino group, alkoxyl, aryloxyl, aminoalkyl, aminoaryl, thio group, sulfonyl, sulfinyl or halogen.

Provided are methods for the treatment of a rheumatic disease, such as rheumatoid arthritis, ankylosating spondylitis and/or polymyalgia rheumatic, by administering a delayed-release dosage form of a glucocorticoid to a subject in need thereof wherein the treatment is administered once daily for at least about two weeks. Also provided are methods for the treatment of osteoarthritis by administering a delayed-release dosage form of a glucocorticoid to a subject in need thereof wherein the treatment is administered once daily for at least about two weeks.

It is reported herein that certain muscle diseases and conditions, including forms of muscular dystrophy, are characterized by impaired insulin-dependent signaling in the muscle tissue, in essence, a form of insulin resistance. The present disclosure relates to therapeutic agents, compositions and methods for treating a muscle disease or condition characterized by impaired insulin-dependent signaling by targeting components of the defective insulin signaling pathway. The disease or condition may be treated by administering a therapeutic agent that activates the insulin signaling pathway, in particular, therapeutic agents that act post-insulin receptor to modulate intracellular effector molecules. An exemplary modulator is metformin. Metformin may be administered alone or may be coadministered with another therapeutic agent for treating the muscle disease or condition, such as a corticosteroid.

Described herein are methods and compositions for enhancing pathogen immunity in plants and improving plant growth.

Compositions and methods, including novel homogeneous microparticulate suspensions, are described for treating natural surfaces that contain bacterial biofilm, including unexpected synergy or enhancing effects between bismuth-thiol (BT) compounds and certain antibiotics, to provide formulations including antiseptic formulations. Previously unpredicted antibacterial properties and anti-biofilm properties of disclosed BT compounds and BT compound-plus-antibiotic combinations are also described, including preferential efficacies of certain such compositions for treating certain gram-positive bacterial infections, and distinct preferential efficacies of certain such compositions for treating certain gram-negative bacterial infections.

The methods described herein enable the evaluation of compounds on subjects to assess their therapeutic efficacy or toxic effects. The target of analysis is the underlying biochemical process or processes (i.e., metabolic process) thought to be involved in disease pathogenesis. Molecular flux rates within the one or more biochemical processes serve as biomarkers and are quantitated and compared with the molecular flux rates (i.e., biomarker) from control subjects (i.e., subjects not exposed to the compounds). Any change in the biomarker in the subject relative to the biomarker in the control subject provides information to evaluate therapeutic efficacy of an administered drug or a toxic effect and to develop the compound further if desired. In one aspect of the invention, stable isotope-labeled substrate molecules are administered to a subject and the label is incorporated into targeted molecules in a manner that reveals molecular flux rates through metabolic pathways of interest.

A method for enabling D2T2 printing onto non-D2T2 printable substrates uses a diffusible primer material provided on a dye-sheet or ribbon. The primer comprises a polymer, a release agent and a plasticizer. The release agent and the plasticizer are diffused into the substrate, while the polymer remains on the dye-sheet or ribbon. Printing of the primer onto the PC substrate is controlled via a computer image program corresponding to a colored image. This computer image program also controls the printing of the colored image at the primed locations. Accordingly, image-wise treatment of a plastic material via the primer selectively renders the PC substrate surface D2T2 printable at the point of personalization, providing for a 100% PC full card body having the colored image.

A method and an apparatus is disclosed that uses a gas lift tubing arrangement to produce synthetic hydrocarbon related products. Using the Fischer Tropsch process as an example, the tubing is packed with a suitable catalyst and then hydrogen and carbon monoxide are injected into the top of the tubing in a fashion similar to a gas lift process. As the gases travel past the catalyst, synthetic hydrocarbons are formed and heat is rejected. The synthetic hydrocarbons and water flow out of the bottom of the tubing and travel up the annulus to the surface. In some embodiments, this process is carried out in a producing well or a in producing riser. In a producing well or a producing riser, the production from the well which flows up the annulus cools the synthetic hydrocarbon derived products. In additional and alternate embodiments, this process can be used in non-flowing wells.

A method and an apparatus is disclosed that uses a gas lift tubing arrangement to produce synthetic hydrocarbon related products. Using the Fischer Tropsch process as an example, the tubing is packed with a suitable catalyst and then hydrogen and carbon monoxide are injected into the top of the tubing in a fashion similar to a gas lift process. As the gases travel past the catalyst, synthetic hydrocarbons are formed and heat is rejected. The synthetic hydrocarbons and water flow out of the bottom of the tubing and travel up the annulus to the surface. In some embodiments, this process is carried out in a producing well or a in producing riser. In a producing well or a producing riser, the production from the well which flows up the annulus cools the synthetic hydrocarbon derived products. In additional and alternate embodiments, this process can be used in non-flowing wells.

The portable, wearable, proximal Alzheimer's disease treatment invention is based upon creating an RF field of particular frequencies and intensities that are applied to the patient's head. To accomplish the aforementioned disease treatment functionality, a system was invented comprising a network of antennas connected to an RF generator via a feedline connector. The invention also provides methods for using measurements to monitor and manage the effectiveness of an ongoing disease treatment regimen, and databases which contain information about measurements, variables, and their relationships to clinical outcome.

An apparatus of muscle and/or joint exercise adapted to reeducation and/or physical conditioning of a body part of a user, including a hollow body; a mobile member translatable inside the body; a motor-driven means arranged on the bottom of the body and adapted for driving and decelerating in displacement the mobile member in both translatory directions; a cooperating means between the body and a frame, this cooperating means being arranged on the bottom of the body and including: at least one hinge of the body on the frame, the hinge providing at least at least one degree of rotational freedom for the body around an axis of rotation; and a removable means for installing and removing the body, the removable means being designed for removably installing the body on a base slidingly mounted in the frame.

A stretching and exercise device has a first strap member having a pair of end portions, a second strap member having a pair of end portions, and a pair of resistance members connected between respective end portions of the first and second strap members. A third strap member has a pair of end portions attached to respective first and second surface portions of the second strap member. A fourth strap member is attached to a third surface portion of the second strap member different from the first and second surface portions thereof.

A pneumatic compression garment includes a flexible member for placement on a limb of a human body. A bladder in the flexible member defines an inflatable chamber. The bladder has an opening through which the inflatable chamber is inflated. A port mounted on the bladder has an air inlet adapted for communication with a source of pressurized air and an air outlet in communication with the inflatable chamber via the opening in the bladder. Pressurized air is delivered from into the inflatable chamber for inflating the inflatable chamber and thereby applying a compression force to the limb when the flexible member is in place on the limb. An air diverter affixed to an inside surface of a first sheet of the bladder and configured to divert air entering the inflatable chamber from directly impinging against an inside surface of a second sheet of the bladder.

The present invention is directed to compositions and processes for their use that ameliorate the appearance of bruises, making them less cosmetically unappealing. The composition functions by acting both as a humectant and occlusive agent attracting water, returning the skin surface to a smooth state and holding water in place. The re-establishment of a homeostatic state in the skin results in a rapid dissipation of the negative cosmetic effects of the bruise on the skin.

Distributed mobile device management including a plurality of management agents is disclosed. Management-related information may be retrieved from a storage location accessible to a plurality of management agents. The management-related information may have been provided to the storage location from a management agent associated with a managed application. And at least one operation may be performed based at least in part on the management-related information.

Provided are a system and method for protecting data in an electronic communications environment. An interested entity establishes one or more controls for a received unit of data. At a source device in the electronic communications network, the unit of data is encapsulated with self-protection security data that includes the one or more controls. The encapsulated unit of data is delivered from the source device to a destination device in the electronic communications network. A data broker facilitates the delivery of the data to the destination device according to the controls. Facilitating the delivery of the data includes: identifying for the receiving device a collection of services corresponding to the controls independently of the network.

This invention relates to methods for improved cell-based therapies for retinal degeneration and for differentiating human embryonic stem cells and human embryo-derived into retinal pigment epithelium (RPE) cells and other retinal progenitor cells.

Provided herein are mitochondrial-nuclear exchanged cells and animals comprising mitochondrial DNA (mtDNA) from one subject and nuclear DNA (nDNA) from a different subject. Methods for producing a mitochondrial-nuclear exchanged animal and animals made by the methods are provided. Also provided are methods of screening for agents useful for treating a disease or disorder using mitochondrial-nuclear exchanged animals or cells, tissues or organs thereof.

Disclosed are compounds which inhibit the activity of anti-apoptotic Bcl-2 or Bcl-xL proteins, compositions containing the compounds and methods of treating diseases during which are expressed anti-apoptotic Bcl-2 protein.