Harrison's Internal Medicine Chapter 23. Weakness and Paralysis
Weakness and Paralysis: Introduction Normal motor function involves integrated muscle activity that is modulated by the activity of the cerebral cortex, basal ganglia, cerebellum, and spinal cord. Motor system dysfunction leads to weakness or paralysis, which is discussed in this chapter, or to ataxia (Chap. 368) or abnormal movements (Chap. 367). The mode of onset, distribution, and accompaniments of weakness help to suggest its cause.
Weakness is a reduction in the power that can be exerted by one or more muscles.
We prove that a typical interval exchange transformation is either weakly mixing or it is an irrational rotation. We also conclude that a typical translation ﬂow on a typical translation surface of genus g ≥ 2 (with prescribed singularity types) is weakly mixing.
Lower Motor Neuron Weakness
This pattern results from disorders of cell bodies of lower motor neurons in the brainstem motor nuclei and the anterior horn of the spinal cord, or from dysfunction of the axons of these neurons as they pass to skeletal muscle (Fig. 232). Weakness is due to a decrease in the number of muscle fibers that can be activated, through a loss of α motor neurons or disruption of their connections to muscle. Loss of γmotor neurons does not cause weakness but decreases tension on the muscle spindles, which decreases muscle tone and attenuates the stretch reflexes...
Upper Motor Neuron Weakness This pattern of weakness results from disorders that affect the upper motor neurons or their axons in the cerebral cortex, subcortical white matter, internal capsule, brainstem, or spinal cord (Fig. 23-1). Such lesions produce weakness through decreased activation of the lower motor neurons. In general, distal muscle groups are affected more severely than proximal ones, and axial movements are spared unless the lesion is severe and bilateral.
Quadriparesis or Generalized Weakness
Generalized weakness may be due to disorders of the CNS or of the motor unit. Although the terms quadriparesis and generalized weakness are often used interchangeably, quadriparesis is commonly used when an upper motor neuron cause is suspected, and generalized weakness when a disease of the motor unit is likely.
Weakness from CNS disorders is usually associated with changes in consciousness or cognition, with spasticity and brisk stretch reflexes, and with alterations of sensation.
If the weakness is predominantly in distal and nonantigravity muscles and not associated with sensory impairment or pain, focal cortical ischemia is likely (Chap. 364); diagnostic possibilities are similar to those for acute hemiparesis.
Sensory loss and pain usually accompany acute lower motor neuron weakness; the weakness is commonly localized to a single nerve root or peripheral nerve within the limb but occasionally reflects plexus involvement.
Presupposition relations between verbs are not very well covered in existing lexical semantic resources. We propose a weakly supervised algorithm for learning presupposition relations between verbs that distinguishes ﬁve semantic relations: presupposition, entailment, temporal inclusion, antonymy and other/no relation. We start with a number of seed verb pairs selected manually for each semantic relation and classify unseen verb pairs. Our algorithm achieves an overall accuracy of 36% for type-based classiﬁcation. ...
Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web’s natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors.
Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures, we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. ...
Named Entity recognition (NER) is an important part of many natural language processing tasks. Current approaches often employ machine learning techniques and require supervised data. However, many languages lack such resources. This paper presents an (almost) unsupervised learning algorithm for automatic discovery of Named Entities (NEs) in a resource free language, given a bilingual corpora in which it is weakly temporally aligned with a resource rich language.
We investigate automatic classiﬁcation of speculative language (‘hedging’), in biomedical text using weakly supervised machine learning. Our contributions include a precise description of the task with annotation guidelines, analysis and discussion, a probabilistic weakly supervised learning model, and experimental evaluation of the methods presented. We show that hedge classiﬁcation is feasible using weakly supervised ML, and point toward avenues for future research.
In the general framework of a constraint-based grammar formalism often some sort of feature logic serves as the constraint language to describe linguistic objects. We investigate the extension of basic feature logic with subsumption (or matching) constraints, based on a weak notion of subsumption. This mechanism of oneway information flow is generally deemed to be necessary to give linguistically satisfactory descriptions of coordination phenomena in such formalisms.
We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntactically parsed corpus, we automatically learn a syntactic model - a set of weighted syntactic features, i.e.
This paper examines unsupervised approaches to part-of-speech (POS) tagging for morphologically-rich, resource-scarce languages, with an emphasis on Goldwater and Grifﬁths’s (2007) fully-Bayesian approach originally developed for English POS tagging. We argue that existing unsupervised POS taggers unrealistically assume as input a perfect POS lexicon, and consequently, we propose a weakly supervised fully-Bayesian approach to POS tagging, which relaxes the unrealistic assumption by automatically acquiring the lexicon from a small amount of POS-tagged data....
Trong tiếng Anh có những từ không mang ngữ nghĩa mà chỉ có giá trị về mặt chức
năng ngữ pháp gọi là function words (từ chức năng), bao gồm liên từ
(conjunction), giới từ (preposition), trợ động từ (auxiliary verb)…. Những từ này
khi phát âm có 2 dạng gọi là âm mạnh và yếu (strong form, weak form).
Trong tiếng Anh, có những từ ngữ không mang ngữ nghĩa mà chỉ có giá trị về mặt chức năng ngữ pháp còn gọi là function words (từ chức năng), bao gồm liên từ (conjunction), giới từ(preposition) và trợ động từ (auxiliary verb). Một số liên từ, giới từ hoặc đại từ có dạng phát âm mạnh và yếu như sau:
Word Strong form
/ði/ - Đứng trước nguyên âm Ex: Hoa
/ðə/ - Đứng trước phụ âm have Ex: I dislike the man.
bought the apples.
/b t/ Ex: I’m but a fool.
Phát âm dạng mạnh (strong form) và dạng yếu (weak form) không phải là một khái niệm xa lạ đối với những người học tiếng Anh, thế nhưng bạn có tự tin là mình nắm vững các nguyên tắc căn bản nhất của dạng này
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Fixed Point Theorems for Generalized Weakly Contractive Condition in Ordered Metric Spaces
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Common End Point Theorem for Set-Valued Generalized ψ, ϕ -Weak Contraction